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Ontology 101: An Introduction to Knowledge
Representation, the Web Ontology Language (OWL) &
Ontology Development
Elisa Kendall, Thematix & Deborah McGuinness, RPI / McGuinness Associates
6 June 2011
+ Content management
                          Mars was photographed by the Hubble Space Telescope in August 2003 as     2     2
                          the planet passed closer to Earth than it had in nearly 60,000 years.
                          Image Credit: NASA, J. Bell (Cornell U.) and M. Wolff (SSI)
                                                            A sunset on Mars creates a glow due to the
                                                            presence of tiny dust particles in the
                                                            atmosphere. This photo is a combination of
                                                            four images taken by Mars Pathfinder, which
                                                            landed on Mars in 1997. Image credit:
                                                            NASA/JPL




  Recent images from instruments on board the
  Mars Reconnaissance Orbiter take much more
  detailed, narrower views of specific features of the
  Martian surface. Image credit: NASA/JPL

         The Planetary Data Store (PDS) is a distributed repository of 40+ years’ imagery & data
           taken by a range of instruments on many diverse missions, available for scientific
                                                research.
Copyright © 2011 Thematix & McGuinness Associates
+ Smart search
                                                                                                    3   3




  Provenance/sources for tracking family members in the 19th
  century include early census data (often error prone), military
  records, passenger & immigration lists, online documents (e.g.,
  county histories, church histories, etc.)

          • Historical/forensic research requires cross-domain search of a wide variety of resources
          within a given geo-spatial/temporal context
          • Similar capabilities are essential for business intelligence, law enforcement, government
          applications – all require terminology reconciliation

Copyright © 2011 Thematix & McGuinness Associates
+ Merchandising
                                                                                         4   4
                                                                Attribute 1
                                                                Attribute 2
                                                                Leg Room
                                                                Attribute 4
                                                                Attribute 5
                                                                Attribute 5
                                                                On-Off Access
                                                    “Comfort-   Attribute 7
                                                                Attribute 8
                                                    Oriented”   Attribute 9
                                                      Flight    Media Options
                                                                Attribute 11
                                                                Attribute 12
                                                                Meal Options
                                                                Attribute 14
                                                                Attribute 15
                                                                Time of Day
                                                                Attribute 17
                                                                Attribute 18
                                                                Distance to Gate
                                                                Attribute 20
                                                                Attribute 21
                                                                Flight Characteristics
                                                                Attribute N
                                                                …


Copyright © 2011 Thematix & McGuinness Associates
+ Search Engine Optimization
                                                                  5   5




         Use search engines as a front door to transaction

         Dramatically increase relevance by inclusion of more
          meaningful, synonymic tags

         Enrich search results, adding ratings, video, prices,
          avails, amenities, locality etc.




Copyright © 2011 Thematix & McGuinness Associates
+ Tutorial Outline
                                                    6   6



       Introduction to Knowledge Representation
       OWL Basics
       Tools & Applications




Copyright © 2011 Thematix & McGuinness Associates
+ Part 1: Introduction to Knowledge Representation
                                                            7   7


             A little background and a few definitions
             Layers of abstraction & conceptual modeling
             Classifying ontologies
             A little methodology




Copyright © 2011 Thematix & McGuinness Associates
+ Definitions
                                                                                                 8   8
            An ontology is a specification of a conceptualization.
             – Tom Gruber
            Knowledge engineering is the application of logic and ontology to the task of
             building computable models of some domain for some purpose. – John Sowa
            Artificial Intelligence can be viewed as the study of intelligent behavior
             achieved through computational means. Knowledge Representation then is
             the part of AI that is concerned with how an agent uses what it knows in
             deciding what to do. – Brachman and Levesque, KR&R
            Knowledge representation means that knowledge is formalized in a symbolic
             form, that is, to find a symbolic expression that can be interpreted. – Klein and
             Methlie
            The task of classifying all the words of language, or what's the same thing, all
             the ideas that seek expression, is the most stupendous of logical tasks.
             Anybody but the most accomplished logician must break down in it utterly;
             and even for the strongest man, it is the severest possible tax on the logical
             equipment and faculty. – Charles Sanders Peirce, letter to editor B. E. Smith of
             the Century Dictionary

Copyright © 2011 Thematix & McGuinness Associates
+ What is an Ontology?
     An ontology specifies a rich description of the
                                                                                           9   9
        Terminology, concepts, nomenclature

        Properties explicitly defining concepts

        Relations among concepts (hierarchical and lattice)

        Rules distinguishing concepts, refining definitions and relations (constraints,
         restrictions, regular expressions)

         relevant to a particular domain or area of interest.




Copyright © 2011 Thematix & McGuinness Associates
+ Logic and Ontology
                                                                                                                              10 10
          Predicate logic is harder to read than the original English, but is
           more precise:
              Every semi-trailer truck has at least 3 axles.

              (∀ x)(((SemiTrailerTruck(x) ∧ (∃ y)(SemiTrailer(y) ∧ (hasPart (x,y))) ∧
                  (SemiTrailerTruck(x) ∧ (∃ z)(TractorUnit(z) ∧ (hasPart (x,z))))
                    ⊃ (∃ s)(set(s) ∧ (count(s,(≥3))
                    ∧ (∀ w)(member(w,s) ⊃ (Axle(w) ∧ hasPart(x,w))) )).

          Logic is a simple language with few basic symbols.
          The level of detail depends on the choice of predicates – these
           predicates represent an ontology of the relevant concepts in the
           domain.
          Different choices of predicates represent different ontological
           commitments.
                                 * Derived from Knowledge Representation: Logical, Philosophical, and Computational Foundations,
                                                                               John F. Sowa, Brooks/Cole, Pacific Grove, CA, 2000.


Copyright © 2011 Thematix & McGuinness Associates
+ Ontology-based Technologies
                                                                                11 11
       Ontologies provide a common vocabulary for use by independently
        developed resources, processes, services
       Agreements among organizations sharing common services can be
        made with regard to their usage; the meaning of relevant concepts can
        be expressed unambiguously
       By composing / mapping ontologies and mediating terminology across
        participating events, resources and services, independently-developed
        services can work together to share information and processes
        consistently, accurately, and completely
       Ontologies also ensure
           Valid conversations among agents to collect, process, fuse, and
            exchange information
           Accurate searching by ensuring context using concept definitions
            and relations instead of/in addition to statistical relevance of
            keywords
Copyright © 2011 Thematix & McGuinness Associates
+ Features of KR languages
        Vocabulary – a collection of symbols                                             12 12
            Domain-independent logical symbols (e.g., ∀ or ⊃)
            Domain-dependent constants, identifying individuals, properties, or
             relations in the application domain or universe of discourse
            Variables, whose range is governed by quantifiers
            Punctuation that separates or groups other symbols
        Syntax –
            formation rules that determine how symbols can be combined in well-
             formed expressions
            rules may be stated in a linear grammar, graph grammar, or independent
             abstract syntax
        Semantics –
            a theory of reference that determines how the constants and variables
             are associated with things in the universe of discourse
            a theory of truth that distinguishes true statements from false statements
        Rules of Inference –
            rules that determine how one pattern can be inferred from another
            if the logic is sound, the rules of inference must preserve truth as
             determined by the semantics
Copyright © 2011 Thematix & McGuinness Associates
+ Logic
     Logics vary from classical FOL along six dimensions:                                                   13 13
           Syntax
           Subsets limit permissible operators or combinations, e.g., propositional logic
            (without quantifiers), Horn-clause (excludes disjunctions in conclusions, such as
            Prolog), terminological or definitional logics (containing additional restrictions,
            e.g., description logics)
           Proof Theory restricts or extends permissible proofs:
            •   Intuitionistic logic and relevance logic rule out certain extraneous information
            •   Non-monotonic logics allow introduction of default assumptions
            •   Access-limited logic restricts the number of times a proposition can be used in a proof;
                Linear logic allows a proposition to be used only once
            •   Modal logic incorporates modal auxiliaries (◊p means p is possibly true; p means p is
                                                                                         
                necessarily true), temporal logic extends model logic to include always, sometimes
            •   Intensional logics express concepts such as need, ought, hope, fear, wish, believe, know,
                expect, and intend
           Model Theory modifies the truth value of statements in terms of some model of the
            world: classical FOL is two-valued; a three-valued logic introduces unknowns;
            fuzzy logic uses the same notation as FOL but with an infinite range of certainty
            factors (1.0 to 0.0)
           Ontology – frameworks may include support for built-in components, such as set
            theory or time
           Meta-language – language for encoding information about objects
Copyright © 2011 Thematix & McGuinness Associates
+ Description logic
         A family of logic-based Knowledge Representation formalisms                      14 14
             Descendants of semantic networks and KL-ONE
             Describe domain in terms of concepts (classes), roles (relationships), and
              individuals (instances)
         Distinguished by
             Formal semantics
              • Decidable fragments of FOL
              • Closely related to Propositional, Modal, and Dynamic Logics
             Provision of inference services
              • Sound and complete decision procedures for key problems
              • Implemented systems (highly optimized)

         Applications include
             Configuration – product configurators, consistency checking, constraint
              propagation, first significant industrial application (e.g., CLASSIC)
             Ontologies – ontology engineering (design, maintenance, integration),
              reasoning with ontology-based mark-up, service description and discovery
             Databases – consistency of conceptual schemata, schema integration, query
              subsumption (w.r.t. conceptual schemata)

Copyright © 2011 Thematix & McGuinness Associates
+ Knowledge bases, databases, & ontology
                                                                                             15 15
          An ontology is a conceptual model of some aspect of a particular
           universe of discourse (or of a domain of discourse)
          Typically, ontologies contain only “rarified” or “special” individuals,
           metadata, representing elemental concepts critical to the domain
          A knowledge base is a persistent repository for
              Ontology & metadata representing individuals, facts, & rules about how they
               can be combined or relate to one another
              Metadata, facts & rules only – in some applications and frameworks the
               ontology is separately maintained

          Most inference engines require in-memory deductive databases for
           efficient reasoning (including commercially available reasoners)
          A knowledge base may be implemented in a physical, external
           database, such as a relational database, but reasoning is typically
           done on a subset (partition) of that knowledge base in memory


Copyright © 2011 Thematix & McGuinness Associates
+ Reasoning & Truth Maintenance
                                                                                         16 16
         Reasoning is the mechanism by which the assertions made in an
          ontology and related knowledge base are evaluated by an inference
          engine.
         In classical logic, the validity of a particular conclusion is retained
          even if new information is received.
         This may change if some of the preconditions are actually
          hypothetical assumptions invalidated by the new information.
         The same idea applies for arbitrary actions – new information can
          make preconditions invalid.
         Generally, there are two issues that a reasoner must address:
             If some conclusion is invalid, which other conclusions are also invalid?
             If some action cannot be performed, which others are at risk?
         The “housekeeping” associated with tracking the threads that support
          answering these questions is called truth maintenance.


Copyright © 2011 Thematix & McGuinness Associates
+ Negation
          If all new information is “positive”, then all prior conclusions should   17 17
           remain valid.
          Problems arise if new information negates a prior assumption,
           causing it to be withdrawn.
          What does it mean to negate (withdraw) an assumption?
              Conclusive information is not available?
              The assumption cannot be proven?
              The assumption is not provable using certain methods?
              The assumption is not provable given a fixed quantity of time?
          The answers to these questions can result in different definitions of
           negation and differing interpretations by non-monotonic reasoners.
          Solutions include chronological and “intelligent” backtracking
           algorithms, heuristics, circumscription algorithms, justification or
           assumption based retraction, and so forth, depending on the reasoner
           and methods used for truth maintenance.
          Reasoning efficiency is dependent, in part, on the algorithms applied
           for truth maintenance.

Copyright © 2011 Thematix & McGuinness Associates
+ Explanations and Proofs
                                                                                      18 18
        When a reasoner draws a particular conclusion, many users and
         applications want to understand why?
        Primary motivations include interoperability, reuse, and trust
        Understanding the provenance of the information and results is
         crucial, especially when web-based information is involved
            What information sources were used (source)
            How recently they were updated (currency)
            How reliable these sources are (authoritativeness)
            Was the information directly available or derived, and if derived, how
             (method of reasoning)
        Methods used to explain why a reasoner reached a particular
         conclusion include explanation generation and proof specification




Copyright © 2011 Thematix & McGuinness Associates
+ Abstraction Layers
                    • Identify subject areas
       Contextual                                                                                                                  19 19
                                                                                              Ontology

       Conceptual
                    • Define the meaning of things in the organization


                    • Describe the logical representation of properties                    Ontology, Conceptual ER, Conceptual
        Logical
                                                                                           Business Process …

                    • Describe the physical means by which data is stored                 ER, Relational, XML Schema
        Physical


                                                                                          XML, source code, scripting languages,
                    • Represent the coding language on a specific development platform    stored procedures…
       Definition


                                                                                          Physical KBs,
                    • Hold the values of the properties applied to the data in a schema   databases, asset repositories…
        Instance

                                                                                          *Layering diagram courtesy Kenn Hussey

     Knowledge Representation / Management for Large Scale Applications
       Provide broad metadata, process, service & asset management facilities (including
        feedback/lessons learned…)
       Enable rich cross-domain, cross-process, cross organizational modeling supported by
        mapping & transformation services to provide maximum flexibility, interoperability
       Leverage standards and best practices in information architecture, metadata modeling,
        management, registration, and governance, and asset management & registration
       Provide incremental reasoning capabilities for model validation, transformation services

     Repeatable, reusable, interoperable
Copyright © 2011 Thematix & McGuinness Associates
+ Hypothetical Conceptual Model “EU-Rent”
                                                                                      20 20




Produced using Embarcadero EA/Studio Business Modeler Edition, courtesy Kenn Hussey

Copyright © 2011 Thematix & McGuinness Associates
+ Hypothetical Logical Model “EU-Rent”
                                                                21 21




   Produced using Embarcadero ER/Studio, courtesy Kenn Hussey
Copyright © 2011 Thematix & McGuinness Associates
+ Hypothetical Physical Model “EU-Rent”
                                                             22 22




Produced using Embarcadero ER/Studio, courtesy Kenn Hussey

Copyright © 2011 Thematix & McGuinness Associates
+ Classifying ontologies
                                                                                                                                  23 23
  Classification techniques are as diverse
  as conceptual models; and generally
  include understanding
                                                                                                                      KR System
     Level of Expressivity
     Level of Complexity / Structure
                                                                                                               OO Software Model
     Granularity
                                                                                                               Entity – Relationship
     Target Usage, Relevance                                                                                          Model
     Amount of Automation, Reasoning Requirements                                               Concept Map
     Prescriptive vs. Descriptive / Reliability / Level                                                    Topic Map
      of Authoritativeness



                                                           Level of Expressivity
     Design Methodology                                                                                     Database Schema

     Governance                                                                                         XML Schema

     Vocabulary Management, Metrics                                                            Hierarchical Taxonomy
                                                                                         Simple Taxonomy
                                                                                   Glossary

                                                                                   Level of Complexity
Copyright © 2011 Thematix & McGuinness Associates
+ Framework of dimensions
                                                                                                                      24 24
          Semantic Dimensions
              Expressiveness: represents how well a KR language addresses
               increasingly complex semantics
              Structure: represents how well an ontology encodes semantics, with
               the same or less expressivity than the KR language
              Granularity: represents the level of detail specified in an ontology
          Pragmatic Dimensions
              Intended use: the original use case(es), or purpose for developing a
               particular ontology
              Automated reasoning: the extent to which the ontology is designed
               to be used for automated reasoning
              Prescriptive vs. Descriptive: the extent to which an ontology was
               intended to be used for descriptive purposes vs. normative
               prescriptive use (i.e., with high degree of concern for correctness)
                                              Reference: http://ontolog.cim3.net/cgi-bin/wiki.pl?OntologySummit2007

Copyright © 2011 Thematix & McGuinness Associates
+ Model dynamics
                                                                                                    25 25
     Model centric perspectives characterize the ontologies
     themselves and are concerned with their structure, formalism
     and dynamics.
      Perspective                One Extreme                     Other Extreme
      Level of                   Least authoritative, broader    Most authoritative, narrower,
      Authoritativeness          shallowly defined ontologies    more deeply defined ontologies
      Source of                  Passive (Transcendent) -        Active (Immanent) - Structure
      Structure                  Structure originates outside    emerges from data or behavior
                                 the system
      Degree of                  Informal or primarily           Formal, having rigorously
      Formality                  taxonomic                       defined types, relations, and
                                                                 theories or axioms
      Model Dynamics             Read-only, ontologies are       Volatile, ontologies are fluid &
                                 static                          changing
      Instance                   Read-only, resource instances   Volatile, resource instances
      Dynamics                   are static                      change continuously



Copyright © 2011 Thematix & McGuinness Associates
+ Application characteristics
                                                                                                  26 26

      Application centric perspectives are concerned with how
      applications use and manipulate ontologies.


       Perspective                 One Extreme                      Other Extreme
       Control/Degree              Externally focused, public       Internally focused, private
       of Manageability            (little or no control)           (full control)
       Application                 Static (with periodic updates)   Dynamic
       Changeability
       Coupling                    Loosely-coupled                  Tightly-coupled
       Integration Focus           Information integration          Application integration
       Lifecycle Usage             Design Time                      Run Time




Copyright © 2011 Thematix & McGuinness Associates
+ Language evaluation
                                                                                                                                    27 27
       KR Expressivity Requirements                                       Reasoning Requirements
       Classes
                                                                          Exceptions
           Slots/Attributes
                                                                          Automatic Classification
           Metaclasses
           Number Restrictions

           Complex Class Extensions (union, intersection)                 Inheritance
           Subsumption Hierarchies                                                Monotonic, Nonmonotonic
           Value Restrictions                                                     Simple, Multiple

       Behaviors, Procedural Definitions, Methods                         Procedural Support, Execution
       Relations / Functions
           Slots/Attributes
           Subsumption Hierarchies                                        Constraint Checking, Deduction
           Built-in Functions, Equations, Formulae
       Instances / Individuals / Facts
       Axioms
                                                                          Reasoning with Rules
       Production Rules                                                   (forward and backward chaining)

             * Derived from “Evaluating Knowledge Representation and Reasoning Capabilities of Ontology Specification Languages”,
                                                                         Oscar Corcho and Asuncion Gomez-Perez, January 2002


Copyright © 2011 Thematix & McGuinness Associates
+ Considerations
        Intended use of ontologies, including domain requirements (e.g.,      28 28
         scientific and engineering apps require formulas, units of measure,
         computations that may be challenging to represent)
        Intended use of KRSs that implement them, including reasoning
         requirements, questions to be answered
        For distributed environments, the number and kinds of resources,
         processes, services requiring ontologies – how distributed, how
         unique, developed collaboratively or independently, dynamic
         community participation or static
        What kinds of transformations are required among processes,
         resources, services to support semantic mediation
        Ontology and KRS alignment / de-confliction / ambiguity resolution
         requirements
        Ontology and KRS composition requirements, dynamic vs. static
         composition, in what environment and under what constraints
        Performance, sizing, timing requirements of target environment

Copyright © 2011 Thematix & McGuinness Associates
+ A Little Methodology …
                                                                                   29 29
           Requirements, domain & use case analysis are critical
               Develop initial source/reference material
               Focus on system or application requirements
               Iterative development starting with a “thread” that covers basic
                capabilities can ground the work and prioritize decisions
           Need to understand and communicate
               Architectural trade-offs, cost & technical benefits
               The nature of the information & kinds of questions that need to
                be answered drive the architecture, approach, and ontology
                scoping and design
           Reuse standards and well-tested, available ontologies
            whenever possible



Copyright © 2011 Thematix & McGuinness Associates
+ What to look for
       A controlled vocabulary
                                                                                             30 30
        FAA & IATA airport codes, ACRISS car codes, …
       A hierarchical or taxonomic structure (for query expansion)
        Vehicle, Ground-based Vehicle, Wheeled Vehicle, Powered Vehicle, Automobile,
        Sedan…
       Knowledge supporting structured queries
        Find all available hybrid SUVs or hybrid sedans that can seat four adults within a
           reasonable taxi ride of Planet Hollywood, Las Vegas for 3-4 days the week of
           June 20th
       Efficient inference (i.e., limited expressive power) vs. increased
        expressivity (potentially expensive or resource bounded
        computation)
       Custom reasoning for temporal relations, geospatial, dynamic
        algorithm / equation evaluation, process-specific, conditional
        operations
       Computational tractability


Copyright © 2011 Thematix & McGuinness Associates
+ Start with canonical definitions
        General concepts as well as domain-specific knowledge                           31 31
        Basic starting point – cross-domain definitions
            Namespace definitions, metadata, naming conventions, governance policies
            Commonly used structures & vocabularies, such as domain-specific
             vocabulary & messaging standards, international country & language codes
             (ISO), national postal addressing or other government standards, industry
             best practices
            Common metadata for ontology & schema management (e.g., Dublin Core,
             for documents & models, ISO 1087 for synonyms & similar relations, MIME
             media types for images & multimedia, etc.)
        Domain vocabularies must be prioritized, selected based on business
         requirements, clear ROI
        Common early targets include
            Smart search (pull); customer experience & cross-sell / upsell (push)
            Richer interoperability among trading partners
            Service registration, description, discovery & management
            Asset/artifact repository search & retrieval
            Automated verification

Copyright © 2011 Thematix & McGuinness Associates
+ IDEF5 (Integrated Definition Methods) Ontology
    Capture Method Analysis
                                                                                       32 32
         Layout a high-level architecture for ontology elements
         Identify the relationships among elements – roles, domain,
          interface, process, utility
         For each ontology element
             Describe its domain and scope, how it will be used
             Identify example questions and anticipated/sample answers for the
              application(s) it will support
             Identify key stakeholders, ownership, maintenance, resources for
              instance knowledge
             Describe anticipated reuse/evolution path
             Identify critical standards, resources that it must interoperate with,
              dependencies
         Resources
             http://www.idef.com/IDEF5/html
             http://www.kbsi.com/technology/methods/sbont.htm



Copyright © 2011 Thematix & McGuinness Associates
+ Principles & Methods for Terminology Work
    (ISO 704)
                                                                                 33 33
         Methodology for describing concepts & terms
             Uses ISO 1087 for terminology
             Uses ISO 860 for terminology “harmonization” (alignment) methods
             Basis for typical methods used for taxonomy development today
         Describes how to flesh out definitions
         Recommendations strategies for relating terms to one another
          using standard vocabulary
         ISO 1087 – great resource for language to describe kinds of
          relationships, acronyms & other designations, preferred vs.
          deprecated terms, etc.
         ISO 860 augments this with recommendations for vocabulary
          comparison


Copyright © 2011 Thematix & McGuinness Associates
+    Part 2: OWL Basics

         A quick intro to RDF                                 34 34

         Basic OWL constructs: classes, properties & slots
         Inheritance & constraints
             Class & slot inheritance
             Property, slot & slot value constraints
             Individuals & literals
             Disjoint & equivalence relations
         New features of OWL 2
         A few rules of thumb
             Depth vs. breadth, naming, synonymous terms
             Class vs. property value, class vs. individual
             Inverse properties
             Limiting scope
Copyright © 2011 Thematix & McGuinness Associates
+ Semantic Web
                                                                               35 35
      "The Semantic Web is an extension of the current web in which
      information is given well-defined meaning, better enabling computers
      and people to work in cooperation."
                                                          -- Tim Berners-Lee




Copyright © 2011 Thematix & McGuinness Associates
+ Guiding Principles
                                                                                    36 36
         Historically, knowledge representation and reasoning systems have
          operated under closed world assumptions
         Uncertainty is magnified under open-world, “wild, wild web” conditions,
          making reasoning much more difficult
         Semantic web languages are designed to support less certainty, to
          provide “better” search results, informed answers to questions, not
          absolutes
         Because they are based on XML, such languages can assist businesses in
          leveraging existing investment in mark-up, content, and data
             To augment business intelligence/analysis and knowledge mining
             To support knowledge sharing and collaboration, augment enterprise
              information integration
             Enrich web services and other applications
             Support policy-based applications and ensure compliance with policy

          at a lower cost with higher potential ROI than traditional computing
          methods

Copyright © 2011 Thematix & McGuinness Associates
+ Resource Description Framework (RDF)
                                                                                 37 37

          Describes relationships
          Uses URIs used for naming
          Language has
              graph based model
              RDF/XML serialization (exchange syntax)
              other presentation syntaxes (N3, Turtle, …)

          Specification, W3C presentations, tools are available at
              Semantic Web: http://www.w3.org/standards/semanticweb/
              Linked Data: http://www.w3.org/standards/semanticweb/data
              RDF: http://www.w3.org/standards/techs/rdf#w3c_all

          New RDF “next steps” revision working group under way to
           make specific enhancements, “fixes” to the standard
              RDF Working Group: http://www.w3.org/2011/rdf-wg/wiki/Main_Page


Copyright © 2011 Thematix & McGuinness Associates
+ RDF Notation Options
                                                                                                        38 38
                                                            Subject

                  http://semtech2011.semanticweb.com/travel.owl#Conference

                                                                                Predicate
Graph:                          http://semtech2011.semanticweb.com/travel.owl#heldAt


                                    http://semtech2011.semanticweb.com/travel.owl#HiltonSFUnionSquare
  Object

                                <rdf:Description rdf:ID=“Conference"/>
XML/RDF:                          <semtech:heldAt rdf:resource="#HiltonSFUnionSquare"/>
                                </rdf:Description>


N3:               semtech:Conference semtech:heldAt semtech:HiltonSFUnionSquare .



 Copyright © 2011 Thematix & McGuinness Associates
+ RDF Schema (RDFS)
       An RDF vocabulary that provides for identifying:          39 39

           classes,
           subsumption (inheritance) relations for classes,
           subsumption (inheritance) relations for properties,
           domain and range for properties




Copyright © 2011 Thematix & McGuinness Associates
+ RDF Schema (RDFS)
                                                                                                 40 40
                                             semtech:Hotel
                                                                    rdfs:subClassOf
Graph:                       rdf:type

                             rdfs:Class                                semtech:ConferenceHotel
                                                     rdf:type
                                                                             rdf:type

                                                         semtech:HiltonSFUnionSquare


                       <rdf:Description rdf:ID="Hotel">
XML/RDF:                 <rdf:type rdf:resource="http://www.w3.org/2000/01/rdf-schema#Class"/>
                       </rdf:Description>

                       <rdfs:Class rdf:ID=“ConferenceHotel">
                         <rdfs:subClassOf rdf:resource="#Hotel"/>
                       </rdfs:Class>

                       <semtech:ConferenceHotel rdf:ID=“HiltonSFUnionSquare“/>


 Copyright © 2011 Thematix & McGuinness Associates
+ The Web Ontology Language (OWL)
            Two languages emerged in parallel to address semantic web                           41 41
             requirements
                DAML-ONT, supported by the DARPA/DAML program
                OIL (Ontology Inference Layer) developed by EU & US researchers
            Merged DAML+OIL was submitted to the W3C 2002, formed the
             basis for the WebOnt Working Group
            OWL extends RDF Schema
                Has an RDFS based syntax and reuses some RDF vocabulary (e.g.,
                 subClassOf, domain, range)
                Adds rich primitives and redefines others (transitivity, inverse, cardinality
                 constraints, complex class definitions)
            Describes the structure of a domain in terms of classes and
             properties
            Uses RDFS for class/property membership assertions (ground
             facts), XML Schema Datatypes
            OWL specifications became W3C recommendations in February
             2004
            OWL 2 specifications was adopted in October 2009
Copyright © 2011 Thematix & McGuinness Associates
+ General nature of descriptions
                                                                       42 42
               a WINE

               a LIQUID                             General Categories
               a POTABLE

               grape: chardonnay, ... [>= 1]
               sugar-content: dry, sweet, off-dry   Structured
               color: red, white, rose              Components
               price: a PRICE
               winery: a WINERY

               grape dictates color (modulo skin)   Interconnections
               harvest time and sugar are related   Between Parts



Copyright © 2011 Thematix & McGuinness Associates
+ General nature of descriptions
                                                                               43 43
        Class              a WINE

 Superclass                a LIQUID                             General Categories
                           a POTABLE
Number / Card
Restrictions               grape: chardonnay, ... [>= 1]
                           sugar-content: dry, sweet, off-dry   Structured
   Roles /                 color: red, white, rose              Components
   Properties              price: a PRICE
                           winery: a WINERY
 Value
 Restrictions              grape dictates color (modulo skin)   Interconnections
                           harvest time and sugar are related   Between Parts



 Copyright © 2011 Thematix & McGuinness Associates
+ Description development
                                                                            44 44

      Define domain terms and inter-relationships
          Define concepts in the domain (classes, nouns)
          Identify subclass/superclass relationships
          Identify attributes/properties/slots (verbs)
          Identify any general properties (relations, functions, verbs)
          Restrict slot values

      Define a representative set of individuals – for testing & evaluation,
       prototype development
          Define individuals (instances)
          Define relationships between individuals (filling in slots)




Copyright © 2011 Thematix & McGuinness Associates
+ Classes & class hierarchy
                                                                                    45 45
           A class is a concept in the domain
               Vintage – a wine made from grapes grown in a specified year
               A class of properties (flavor, body, color, sugar…)
           A class is a collection of elements with similar properties
               White wine – wines made from white grapes
               White table wine – wines made from white grapes that are not
                appellations or regional (not “quality wine” in the EU)
           A class contains necessary conditions for membership (specific
            varietal, field, micro-climate, date picked, date bottled)
           Instances of classes
               Andrew Murray 2005 Late Harvest Viognier
               Robert M. Parker, Jr,’s Wine Advocate review, dated February 28, 2002
               Los Olivos, Santa Barbara County, California, USA


Copyright © 2011 Thematix & McGuinness Associates
+ Class inheritance
                                                                                 46 46
          Classes are organized into subclass-superclass (or
           generalization-specialization) hierarchies
          True subclass relationships are the basis of a formal is-a
           hierarchy
           Classes are “is-a” related if an instance of the subclass is an
             instance of the superclass
          Classes may be viewed as sets
          Subclasses of a class are comprised of a subset of the
           superset
          Examples
              RedWine is a subclass of Wine
               Every red wine is a wine or every instance of a red wine (e.g.,
                 Marietta Old Vines Red) is an instance of wine
              NapaValleyWine is a subclass of CaliforniaWine
               Every wine from Napa Valley is a wine from California


Copyright © 2011 Thematix & McGuinness Associates
+ Levels in the class hierarchy
                                                                                       47 47
         Different modes of development
             Top-down - define the most general concepts first and then
              specialize them
             Bottom-up - define the most specific concepts and then organize
              them into more general classes
             Combination (typical – breadth at the top level and depth along a
              few branches to test design)
         Class inheritance is transitive
             A is a subclass of B (white wine, dessert wine are subclasses of
              wine)
             B is a subclass of C (viognier is a subclass of white wine, late
              harvest wine is a subclass of dessert wine)
             therefore A is a subclass of C (late harvest viognier is a subclass of
              white wine, dessert wine, & wine)

Copyright © 2011 Thematix & McGuinness Associates
+ Classes in OWL 2
                                                                               48 48




                   6 kinds of class expressions in OWL
                   5 anonymous
                   Definitions can be nested using set-theoretic constructs
Copyright © 2011 Thematix & McGuinness Associates
+ Properties & slots
                                                                               49 49
          Slots in a class definition describe attributes of members of a
           class
           Each wine will have color, sugar content, flavor, body, etc.
          Types of properties
              “intrinsic” properties: flavor and color of wine
              “extrinsic” properties: name and price of wine
              parts: ingredients in a recipe
              relations to other objects: producer of wine (winery)
          Data and object properties
              simple (datatype) contain primitive values (strings, numbers)
              complex properties may contain other objects (e.g., a winery
               instance)


Copyright © 2011 Thematix & McGuinness Associates
+ Properties in OWL 2
                                                    50 50




Copyright © 2011 Thematix & McGuinness Associates
+ Class & slot inheritance
                                                                                        51 51
      A subclass inherits all the slots from its super class
       If a wine has a name and flavor, a red wine also has a name and flavor
      If a class has multiple super classes, it inherits slots and
       restrictions from all of them
       Latin is both an individual language and an ancient language. It inherits “has
         attested literature: true” from ancient language and “has indigenous
         name: lingua latina” from individual language




Copyright © 2011 Thematix & McGuinness Associates
+ Property (slot) constraints / restrictions
        Number restrictions describe or limit the number of possible values
         a particular property can have                                                  52 52
            A language must be associated with at least one English name and at least
             one French name
            A language may be associated with zero or more Indigenous names
        Cardinality – similar meaning to classical set theory, measures the
         number of elements in the set (restriction class)
            Cardinality – cardinality N means the class defined by the property
             restriction must have exactly N values (individual or literal values)
            Minimum cardinality - 1 means that there must be at least one value
             (required), 0 means that the value is optional
            Maximum cardinality - 1 means that there can be at most one value
             (single-valued), N means that there can be up to N values (N > 1, multi-
             valued)




Copyright © 2011 Thematix & McGuinness Associates
+ Slot value constraints
                                                                                 53 53


       Slot value type – defines the set of possible values for the
        property
           String: a string of characters (“McGinley Vineyard”)
           Number: an integer or a float (3.5 acres)
           Boolean: a true/false flag
           Enumerated type: a list of allowed values (red, white, rose)
           Filler: a single value (e.g., the color slot for a RedWine must be
            filled with the single value “red”)
           Object type – a class defined in an ontology (e.g., Winery is the
            value restriction on the hasMaker slot on the class Wine)




Copyright © 2011 Thematix & McGuinness Associates
+ Domain & range properties
                                                                                       54 54
         In OWL and many other KR languages, relations (properties, slots) are
          strictly binary
         The domain & range represent the source & target arguments,
          respectively, for the property
         Domain of a slot – the class (or classes) that may have the slot − Wine
          is the domain of the slot hasWineColor
         Range of a slot – the class (or classes) to which slot values belong −
          everything that fills the hasWineColor slot is an instance of the
          enumerated class {red, white, rose}
         Some KR languages that inherently support n-ary relations, such as
          CL, do not make this distinction
             More flexible, intuitively more like mathematics, where functions have
              ranges (or return types) but not all relations are functions
             Requires additional relations to specify argument order, which can be
              critical for ontology alignment


Copyright © 2011 Thematix & McGuinness Associates
+ Property inheritance
         A subclass inherits all the slots of its superclass(es)         55 55

         A subclass can add constraints to “narrow” the set of allowed
          values
             Make the cardinality range smaller
             Replace a class in the range with a subclass




Copyright © 2011 Thematix & McGuinness Associates
+ Individuals
                                                                                             56 56
       An Individual (instance, object in other paradigms)
           Any class that an individual is a member of, or is an individual of, is a type
            of the individual
           Any superclass of a class is an ancestor (or type) of the individual
       Specify slot values for the individual
           Slot values should conform to the constraints such as range, value type,
            cardinality restrictions, etc.




Copyright © 2011 Thematix & McGuinness Associates
+ OWL Individuals
                                                                            57 57
                                                    Graco     Garmin

                                                                Chevrolet
                                                       Sony
                                                              BMW




Copyright © 2011 Thematix & McGuinness Associates
+ OWL Statements
                                                                               58 58
                                                    Graco       Garmin

                                                                   Chevrolet
                                                      Sony
                                                                 BMW




                                                    a Mini
                                                    Cooper
                                                                a Camaro LT
                                                    Convertible




Copyright © 2011 Thematix & McGuinness Associates
+ OWL ObjectProperty
                                                                               59 59
                                                    Graco       Garmin

                                                                   Chevrolet
                                                      Sony
<owl:ObjectProperty rdf:ID="builtBy">
                                                                 BMW
 <rdfs:range rdf:resource="#Enterprise"/>
 <rdfs:domain rdf:resource="#DurableGood"/>
 <owl:inverseOf rdf:resource="#hasBuilt"/>
</owl:ObjectProperty>


                                                    a Mini
                                                    Cooper
                                                                a Camaro LT
                                                    Convertible




Copyright © 2011 Thematix & McGuinness Associates
+ OWL ObjectProperty
                                                                                        60 60
                                                     range   Graco       Garmin

                                                                            Chevrolet
                                                               Sony
                                                                          BMW
<owl:ObjectProperty rdf:ID="builtBy">
 <rdfs:range rdf:resource="#Enterprise"/>
 <rdfs:domain rdf:resource="#DurableGood"/>
 <owl:inverseOf rdf:resource="#hasBuilt"/>
</owl:ObjectProperty>

                                                             a Mini
                                                             Cooper
                                                                         a Camaro LT
                                                             Convertible




 Copyright © 2011 Thematix & McGuinness Associates
+ OWL ObjectProperty
                                                                                       61 61
                                                    range
                                                            Graco       Garmin

                                                                           Chevrolet
                                                              Sony
<owl:ObjectProperty rdf:ID="builtBy">                                    BMW
 <rdfs:range rdf:resource="#Enterprise"/>
 <rdfs:domain rdf:resource="#DurableGood"/>
 <owl:inverseOf rdf:resource="#hasBuilt"/>
</owl:ObjectProperty>


                                                            a Mini
                                               domain       Cooper
                                                                        a Camaro LT
                                                            Convertible



Copyright © 2011 Thematix & McGuinness Associates
+ Inverse Properties
                                                                                     62 62
                                                 domain   Graco       Garmin


                                                            Sony         Chevrolet

<owl:ObjectProperty rdf:ID="hasBuilt">                                 BMW
 <rdfs:range rdf:resource="#DurableGood"/>
 <rdfs:domain rdf:resource="#Enterprise"/>
 <owl:inverseOf rdf:resource="#builtBy"/>
</owl:ObjectProperty>


                                                          a Mini
                                            range         Cooper
                                                                      a Camaro LT
                                                          Convertible




 Copyright © 2011 Thematix & McGuinness Associates
+ OWL Class
                                                                          63 63
                                                    Graco    Garmin

                                                              Chevrolet
<owl:Class
                                                      Sony
rdf:ID="ManufacturingEnterprise"/>
                                                             BMW


 <owl:Class rdf:ID="DiscreteManufacturingEnterprise">
 <rdfs:subClassOf>
    <owl:Class rdf:about="#ManufacturingEnterprise"/>
 </rdfs:subClassOf>
 </owl:Class>




Copyright © 2011 Thematix & McGuinness Associates
+ Class Axioms
                                                            64 64



           A ⊆ B where B
      Subsumption (necessary)
                                                    B
       

       is a class description
       partial or primitive class                       A


      Definition (necessary and sufficient)
          C ≡ D where D
                                                    D
       is a class description
       complete or defined class
                                                        C

  Courtesy Evan Wallace, NIST

Copyright © 2011 Thematix & McGuinness Associates
+
    Meta-Properties – Global Cardinality Restriction
                                                                 65 65
                                                    Range
          Functional




                                                            Domain
                                                    Range




          Inverse Functional


  Courtesy Evan Wallace, NIST
                                                            Domain
Copyright © 2011 Thematix & McGuinness Associates
+ Class Descriptions – Property Restriction
                                                                                     66 66

                       property P




                      Individual
                      of Class C



             Quantified property restriction (type)
                 Universally quantified – allValuesFrom
                 Existentially quantified - someValuesFrom

             hasValue property restriction (value)

             Property cardinality restriction (# of values)
                                                               Courtesy Evan Wallace, NIST

Copyright © 2011 Thematix & McGuinness Associates
+ Disjoint classes
                                                    A                          67 67

                                                                               B
         Classes are disjoint if they cannot have common instances
         Disjoint classes cannot have any common subclasses
         If winery and wine are disjoint, then there is no instance that is
          both a winery and a wine; there is no class that is both a
          subclass of winery and a subclass of wine
         Disjointness is often used to aid consistency checking
         Disjointness is also helpful in teasing out subtle distinctions
          among classes across multiple ontologies
         Equivalence is also often used to identify the same concepts
          across ontologies that may be named differently, or to name
          classes defined through class axioms


Copyright © 2011 Thematix & McGuinness Associates
+ New features in OWL 2
                                                                68 68
         New property constructs
             Reflexive, irreflexive, & asymmetric properties
             Property chains
             Disjoint properties
             Keys
             Self-reflexive properties
             Qualified cardinality restrictions
             Negative property assertions

         New class axioms
             Disjoint unions
             Disjoint classes



Copyright © 2011 Thematix & McGuinness Associates
+ More new features
         Extended datatype handling
                                                                                              69 69
             Better support for XML Schema Datatypes
             New datatypes for OWL specifically (owl:real, owl:rational, rdf:PlainLiteral)
             Custom datatype definition & use of xsd facets / datatype restrictions

         New data range restrictions
             Intersections, unions, complements

         Support for punning
             Limited support for a particular element to be defined as both a class &
              individual, for example

         Improved support for annotations (e.g., metadata about ontologies,
          provenance, transformation detail, etc.)

         See http://www.w3.org/TR/owl2-new-features/ &
          http://www.w3.org/TR/owl2-quick-reference/ for detail

         Current work specifically focused on provenance:
          http://www.w3.org/2011/prov/wiki/Main_Page
Copyright © 2011 Thematix & McGuinness Associates
+ Rules of Thumb
                                                     70 70

         Cycles are common in many KR
          systems, though rarely “a good thing”
         Cycles are disallowed by some tools
          because they prohibit “code
          generation”, export -- including
          RDF/OWL
         Classes A, B, and C have equivalent
          sets of instances
             By many definitions, A, B, and C are
              equivalent
             Use owl:equivalentClass instead of
              creating cycles




Copyright © 2011 Thematix & McGuinness Associates
+ Siblings in the class hierarchy
                                                                                            71 71

                                                   All siblings should be specified at
                                                    roughly the same level of generality
                                                   Compare to section and subsections in
                                                    a book




Copyright © 2011 Thematix & McGuinness Associates
+ Class specification
                                                                                                     72 72
                                                       If a class has only one child, there
                                                        may be a modeling problem – often a
                                                        sign that a definition is incomplete
                                                       If the only Red Burgundy we have is
                                                        Côtes d’Or, why introduce the
                                                        subclass?

                                                       Subclasses of a class usually have
                                                           Additional properties
                                                           Additional slot restrictions
                                                           Participate in different relationships
                                                       Compare to bullets in a bulleted list



Copyright © 2011 Thematix & McGuinness Associates
+Creating levels & subclasses
                                                                                               73 73

                                                       If a class has a large number of
                                                        subclasses, it may be useful to
                                                        define intermediate levels
                                                       For example, in the domain of
                                                        wines, there are natural
                                                        groupings around wine color
                                                       However, if no natural
                                                        classification exists, the long list
                                                        may be appropriate




Copyright © 2011 Thematix & McGuinness Associates
+ Inheritance, naming, synonyms
                                                                                          74 74
                                          A “wine” is not a subclass of “wines”

                                          A particular vintage should be classified as
                                           an instance of the class Wines
     Class
                                          Class names should be either
                                              all singular
                                              all plural
                 instance-of
                                          Synonym names for the same concept are not
     Instance                              different classes
    MariettaOldVinesRed
                                          Many systems, metadata standards support
                                           synonymous terms as part of a class definition
                                          OWL allows defining necessary and
                                           sufficiency condition definitions thereby
                                           allowing synonym definitions to be “first
                                           class” terms
Copyright © 2011 Thematix & McGuinness Associates
+ Class vs. property value
                                                                                  75 75




            Do concepts with different slot values become restrictions for
             different slots?
            How important is the distinction for the domain?
            Class definitions for most domains should be fairly stable – i.e.,
             they should not change frequently once the definitions are
             established and individuals created



Copyright © 2011 Thematix & McGuinness Associates
+ Class vs. individual
                                                                               76 76




          Individual instances are the most specific objects in an ontology
          If concepts form a natural hierarchy, represent them as classes
          If they will have instances below them, represent them as classes

Copyright © 2011 Thematix & McGuinness Associates
+ Limiting scope
                                                                                  77 77

          An ontology should not contain all the possible information
           about the domain
                 No need to specialize or generalize more than the application
                  requires
                 No need to include all possible properties of a class
                   •    Only the most salient properties
                   •    Only the properties that the application requires

          Ontologies of wine, food, and their pairings probably will not
           include details such as:
                 Bottle size (half bottle, full bottle, magnum, …)
                 Label color
                 Wine bottle color (green, amber, …)




Copyright © 2011 Thematix & McGuinness Associates
+ Part 3: Putting It All Together
                                                                           78 78
          Syntax Checking
          Consistency Checking
          Instance Data Analysis & Evaluation Services
          Applications
              Lighter Weight
              Richer Applications
          Examples – showing a combination of semantic web
           components in action: TW Wine Agent
          Understanding, Trust & Proof: InferenceWeb
          eScience Applications: Virtual Solar Terrestrial Observatory,
           SONet, & PopSciGrid
          Leveraging Explanations in a Cognitive Assistant
          Questions
Copyright © 2011 Thematix & McGuinness Associates
+
     Syntax checking
    For RDF & OWL Ontologies                                            79 79
        RDF syntax checking, graph visualization
         •   W3C RDF Validator (http://www.w3.org/RDF/Validator/)
         •   Jena API & Toolkit (http://jena.sourceforge.net/)
        OWL syntax checking, OWL dialect
         determination
         •   OWL Consistency Checker
             (http://clarkparsia.com/pellet/, also provides full OWL 2
             DL reasoning capabilities)
         •   OWL 2 Validator (Univ. of Manchester,
             http://owl.cs.manchester.ac.uk/validator/ )
         •   Protégé OWL (http://protege.stanford.edu/)
         •   Jena API & Toolkit (http://jena.sourceforge.net/)

    Every tool provides unique capabilities;
     sophisticated projects may require multiple
     approaches
    Tools listed are open source, commercial
     options are also available
Copyright © 2011 Thematix & McGuinness Associates
+ Consistency checking
         Requirements are typically application specific                                        80 80
         RDF vocabularies should be checked by an RDF validator or rule engine
             Jena Semantic Web Framework - http://jena.sourceforge.net/
             Pychinko, MindSwap’s Rete-based RDF-friendly rule engine – (CWM clone)
              http://www.mindswap.org/~katz/pychinko/
         OWL Ontologies should be run through a consistency checking reasoner
             Pellet (open source, originally from Mindswap, supported by Clark & Parsia, LLC)
              – http://clarkparsia.com/pellet/,
             RacerPro – http://www.racer-systems.com/
             FaCT++ – http://owl.man.ac.uk/factplusplus/
             HermiT OWL Reasoner – http://www.hermit-reasoner.com/
             KAON2 infrastructure for managing OWL-DL, SWRL, and F-Logic ontologies –
              http://kaon2.semanticweb.org/
             VIStology's ConsVISor OWL Consistency checker –
              http://68.162.250.6:8080/consvisor/

         OWL instance data may also be run through checking tools
             TW Instance data evaluation - http://onto.rpi.edu/demo/oie/


Copyright © 2011 Thematix & McGuinness Associates
+ Example process
                                                                                              81 81




                                                            Pellet OWL DL Reasoner

                                                                            Valid OWL
                                                                (DL consistency checked, concept
                                                                     satisfiability checked)




                                                               TW Instance Data
                                                                  Evaluation


                                                                         Valid OWL
                                                                  (FOL consistency checked,
                                                                     deductive closure)


                            Valid RDF/XML Documents
   Ontology Registry    (Valid OWL Syntax Representation)
       & Library                                            Application Environment



   Ontologies should be checked to ensure consistency,
   limit potential for invalid conclusions

Copyright © 2011 Thematix & McGuinness Associates
+ Inconsistencies caused by distributed OWL data
                                                                                                    82 82

   Wine ontology
                                                    wine:Wine
                        rdfs:subClassOf                                rdfs:subClassOf


                wine:EarlyHarvest                                         wine:LateHarvest
                                                    owl:disjointWith



   A new ontology                                               A new instance data
     rdfs:subClassOf                 rdfs:subClassOf              rdf:type               rdf:type

              wine:BadWineClass                                          wi:BadWineInstance

       Case1: semantic inconsistency                             Case2: semantic inconsistency
       caused by a new class.                                    caused by a new instance.
Copyright © 2011 Thematix & McGuinness Associates
+ TW OWL instance-data evaluation
                                                                                   83 83
         Motivation
             Objective metrics are needed to evaluate if semantic web instance
              data is ready for practical use
             Next generation Chimaera for more diverse and more instance-
              oriented applications

         Challenges
             Criteria for “ready for use” vary across applications
             Existing syntax and logical-consistency checking is not enough for
              applications using some standard practical assumptions (e.g.
              closed world, unique names, …)

         Solution
             Extensible & customizable service-oriented architecture
             Integrated environment for evaluating issues beyond standard
              syntactic correctness and logical consistency

         See http://onto.rpi.edu/demo/oie/


Copyright © 2011 Thematix & McGuinness Associates
+ Some issues in OWL instance data
                                                                                           84 84

           owl:cardinality                                   owl:cardinality     "1"
                                    "1"
           owl:onProperty                                    owl:onProperty
                                       hasMaker                                      hasColor
            rdf:type                                          rdf:type
                            owl:Restriction                                 owl:Restriction
rdfs:subClassOf                                      rdfs:subClassOf

     Wine                                              Wine               hasColor      Red
                                                                      W
             W                                             rdf:type
MPV rdf:type                                         EPV                  hasColor      White


          Missing property value (MPV)
          Excessive property value (EPV)


 Copyright © 2011 Thematix & McGuinness Associates
+ “Pluggable” instance evaluation services
   <?xml version="1.0"?>
   <!DOCTYPE rdf:RDF [
   <!ENTITY rdf "http://www.w3.org/1999/02/22-rdf-syntax-ns#" >
   <!ENTITY wine "http://tw.rpi.edu/2008/04/wine-lite.owl#" > ]>
                                                                   85 85
   <rdf:RDF xmlns:wine = "&wine;" xmlns:rdf = "&rdf;">

   <!-- A wine instance missing a value for hasMaker.
       Therefore, we need to report a warning
       indicating a missing property value.
   -->
   <wine:Zinfandel rdf:about="#W1">
    <wine:hasColor rdf:resource="&wine;Red"/>
    <wine:hasFlavor rdf:resource="&wine;Moderate"/>

    <wine:hasSugar rdf:resource="&wine;Dry"/>
    <wine:hasBody rdf:resource="&wine;Full"/>
   </wine:Zinfandel>

   <!--
    A wine instance missing a value for hasColor;
    however, the value for hasColor for all
    instances of Zinfandel has been defined
    in the wine ontology. Therefore, we don't
    need to report a warning indicating a
    missing property value.
   -->
   <wine:Zinfandel rdf:about="#W3">
     <wine:hasFlavor rdf:resource="&wine;Moderate"/>
     <wine:hasSugar rdf:resource="&wine;Dry"/>
     <wine:hasBody rdf:resource="&wine;Full"/>
     <wine:hasMaker>

      <wine:Winery rdf:about="#Elyse" />
    </wine:hasMaker>
   </wine:Zinfandel>

   </rdf:RDF>
Copyright © 2011 Thematix & McGuinness Associates
+ Ontology-enhanced applications
      Simple ‘ontologies’, taxonomy driven applications                                    86 86
          Controlled, shared vocabularies (content management, faceted search)
          Web site organization & navigation, expectation setting
          Browsing & search engine optimization via mapping to tagged structures such as
           the new schema.org from Yahoo!, Google & Bing, Good Relations ontology – see
           http://purl.org/goodrelations/
          “Smarter” search support (query expansion such as FindUR, e-Cyc; SPARQL for
           linked data applications)

      More expressive ontologies provide more options for
          Configuration, e.g., AT&T PROSE/Questar
          Data Integration, e.g., Virtual Observatories (VSTO, …)




Copyright © 2011 Thematix & McGuinness Associates
Semantic Sommelier
+
                                                                            87 87

                                                     Semantically-enabled
                                                     advisors, utilizes

                                                        ontologies

                                                        reasoning

                                                        social

                                                        mobile

                                                        provenance

                                                        context



Copyright © 2011 Thematix & McGuinness Associates
+ TW Wine Agent – semantic interoperability
      Wine Agent receives a meal description and retrieves a           88 88
      selection of matching wines available on the Web, using
      an ensemble of standards and tools
         RDF & OWL − for encoding wine/food listings and pairing
          recommendations
         Semantic MediaWiki − for publishing user-contributed
          recommendations*
         Twitter and Facebook − for posting recommendations
         Pellet − for deriving knowledge from wine ontology and
          recommendations
         SPARQL − for querying wine/food listings with
          recommendations
         Inference Web − for explaining TW Wine Agent’s intelligent
          behavior
Copyright © 2011 Thematix & McGuinness Associates
+ Social Semantic Web data publishing
                                                                                     89 89




                                                    Collaborative wine recommendations
                                                       Using Semantic MediaWiki to
                                                        manage users and user-contributed
                                                        food/wine pairings
                                                        recommendations
                                                       Using semantic form to add OWL
Copyright © 2011 Thematix & McGuinness Associates       instance data
+ Semantic Query:
    food hierarchy & recommendation
                                                    90 90




Copyright © 2011 Thematix & McGuinness Associates
+ Wine Agent processing
                                                                                    91 91

                                                    Given a description of a meal
                                                     Combine wine ontology and the
                                                      OWL data published at Wiki
                                                     Use Pellet and SPARQL to state a
                                                      premise (the meal) and query
                                                      the knowledge base for a
                                                      suggestion for a wine
                                                      description or a set of wine
                                                      instances
                                                     Use Inference Web to explain
                                                      results (descriptions, instances,
                                                      provenance, reasoning engines,
                                                      etc.)




Copyright © 2011 Thematix & McGuinness Associates
+ Wine Agent for iPhone
                                                      92 92

            Client application which talks to a SW
             service

            Make requests for dishes and wines
             using auto-generated interfaces

            Make recommendations to the system
             for others




Copyright © 2011 Thematix & McGuinness Associates
+ Dynamic interfaces as function of ontology
                                                                                  93 93

                                                        owl:Functional
                                                        or
                   rdfs:range ==                        owl:maxCardinality == 1
                   owl:Class



                                   rdfs:range != owl:Class U
                                   rdfs:Literal




                rdfs:range ==
                rdfs:Literal                                     Otherwise




Copyright © 2011 Thematix & McGuinness Associates
+ Descriptions
                                                    94 94
            Resulting descriptions are
             converted to classes (for creating
             recommendations) or to instances
             (for obtaining recommendations)

            When going to a class, all “Is A”
             statements become
             rdfs:subClassOf and all other
             properties become owl:Restrictions

            Instances are realized by the
             reasoner to identify appropriate
             recommendations




Copyright © 2011 Thematix & McGuinness Associates
+ Getting the Recommendation
                                                    95 95
       Recommendations are made up of
        two classes: 1 dish, 1 wine

       When the instance is classified,
        the agent returns the matching
        recommendations

       Tapping a particular
        recommendation causes the wine
        agent to look for items belonging
        to the matching pair




Copyright © 2011 Thematix & McGuinness Associates
+ Extensibility
                                                    96 96
            Wine Agent Ontology is extensible
              “Menu file” in RDF

            Restaurants can introduce new
             classes and instances to provide
             better matches for users

            Searches can be restricted to
             imported menus




Copyright © 2011 Thematix & McGuinness Associates
+ Future Work
                                                                   97 97


            Group recommendations using multiple iPhones/iPod
             touches

            Recommendations based on personal dietary needs and
             preferences

            ‘iPellet’, an OWL Reasoner designed for iPhone, for
             offline reasoning




Copyright © 2011 Thematix & McGuinness Associates
+ Observations from the Wine Agent
                                                                                98 98
      Background knowledge is reasonably simple and built in OWL
       (includes foods and wine and pairing information similar to the OWL
       Guide, Ontology Engineering 101, CLASSIC Tutorial, …)
      Background knowledge can be used for simple query expansion over
       wine sources to retrieve for example documents about red wines
       (including zinfandel, syrah, …)
      Background knowledge used to interact with structured queries such
       as those possible on wine.com
      Constraints allows a reasoner like Pellet to infer consequences of the
       premises and query.
      Explanation system (Inference Web) can provide provenance
       information such as information on the knowledge source
       (McGuinness’ wine ontology) and data sources (such as wine.com)
      Services work could allow automatic “matchmaking” instead of hand
       coded linkages with web resources
Copyright © 2011 Thematix & McGuinness Associates
+ Trust & Understanding
                                                                                 99 99
      If users (humans and agents) are to use, reuse, and integrate system
         answers, they must trust them.
      System transparency supports understanding and trust.
      Even simple “lookup” systems benefit from providing information
        about their sources.
      Systems that manipulate information (with sound deduction or
        potentially unsound heuristics) benefit from providing
        information about their manipulations.



      Goal: Provide interoperable infrastructure that supports explanations of
             sources, assumptions, and answers as an enabler for trust.


Copyright © 2011 Thematix & McGuinness Associates
+ Explanations, Proof Analysis
                                                                             100
                                                                               100

          Framework for explaining question answering tasks
              Stores and manages meta-information about proofs and
               explanations through a distributed repository
              Uses the Proof Markup Language (PML) for proof interchange
               (OWL-based)
          Services include proof presentation, strategy-based views,
           filtering, trust, combination, expansion, checking, search, and
           other capabilities
          Integrated browsing and display of PML documents from
           diverse sources
          Rewriting capabilities for improved understanding
          Multi-modal dialogue options including alternative strategies
           for presenting explanations, visualizations, and summaries



Copyright © 2011 Thematix & McGuinness Associates
+ Inference Web (IW)
                                                                                                            101
                                                                                                              101

                                                                      End
                                                                      Users



                                                         End-User      Data Access &
                                                                                       Validate published PML data
                                                        Interaction    Data Analysis
                                                         services        Services
                           Explanation via Graph




                                                                  Distributed
Explanation via Summary                                            PML data
                           Explanation via Annotation                                  Access published PML data


        Inference Web is a semantic web-based knowledge provenance management
        infrastructure:

        • PML for encoding and interchange provenance metadata in distributed environment
        • Interactive explanation services for end-users
        • Data access and analysis services for enriching the value of knowledge provenance
  Copyright © 2011 Thematix & McGuinness Associates
+ Proof Markup Language (PML)
        A new kind of linked data on the Web                                                            102
                                                                                                           102




                                                          World Wide Web            D
                                                                      PML
                                                        PML           data
                  Enterprise Web                        data
                  PML         PML                                            PML
                  data                              D          PML
                              data                             data          data

                                                           D                            Enterprise Web
              D     D     D          D                                                       PML
                                D                                                            data
                                                                                             …




        Modularized & extensible
          Provenance: annotate provenance properties
          Justification: encodes provenance relations
          Trust: add trust annotation

        Semantic Web based
Copyright © 2011 Thematix & McGuinness Associates
+    Technical Architecture
                                                    IW Registrar
                  call       Web Service                                Edit             edit               103
                                                                                                              103
                                                      PML database
                              Interface                               Interface

machine agents                                               generate                           domain experts
                     generate
                                                                                                           edit
                                                                     PML API
                   translate
   Proofs                        PML Translator
  (N3, KIF)
                                                                PML (provenance, justification, trust)
             Computation Tools
(validation, conflict detection, abstraction)
                                                                            is-stored-as
                                                                                                   Data Access
  Presentation Tools
    (IW Browser)                                     PML Java           PML      parse      PML Web
                                                      Objects            API                Documents
       reads             uses                               harvests & indexes
                                               searches                                                  harvests
                                                             IW Search            subscribes

Copyright © 2011 Thematix & McGuinness Associates
Making Systems Actionable
                                        using Knowledge Provenance
                    Mobile                                                   CALO
                   Wine Agent
                                                                                          104
                                                                                            104




                                                             Combining
                                                            Proofs in TPTP




                                          Intelligence Analyst Tools




                                                                                Knowledge
                                                                                Provenance
                                                                                 in Virtual
GILA                                     NOW including Data-gov                Observatories
Copyright © 2011 Thematix & McGuinness Associates                                        104
+ Example Proof Tree Browser
                                                    105
                                                      105




Copyright © 2011 Thematix & McGuinness Associates
+ Explaining Extracted Entities
                                                      Source: fbi_01.txt (in NL)             106
                                                                                               106
                 Same conclusion                    Source Usage: span from 01 to
                  from multiple                                   78
                    extractors



                                                                           Conflicting
                                                                       conclusion from one
                                                                         extractor (with
                                                                          annotations)



                                                             This extractor decided
                                                            that Person_fbi-01.txt_46
                                                               is a Person and not
                                                              Occupation (in logical
                                                                format – e.g. KIF)



Copyright © 2011 Thematix & McGuinness Associates
+ Trustworthiness of Ramazi report
                                        From FBI
                                                                                 107
                                                                                   107




                                                    From intercepts




                                                                      From CIA



Copyright © 2011 Thematix & McGuinness Associates
Ontology 101 - Kendall & McGuiness
Ontology 101 - Kendall & McGuiness
Ontology 101 - Kendall & McGuiness
Ontology 101 - Kendall & McGuiness
Ontology 101 - Kendall & McGuiness
Ontology 101 - Kendall & McGuiness
Ontology 101 - Kendall & McGuiness
Ontology 101 - Kendall & McGuiness
Ontology 101 - Kendall & McGuiness
Ontology 101 - Kendall & McGuiness
Ontology 101 - Kendall & McGuiness
Ontology 101 - Kendall & McGuiness
Ontology 101 - Kendall & McGuiness
Ontology 101 - Kendall & McGuiness
Ontology 101 - Kendall & McGuiness
Ontology 101 - Kendall & McGuiness
Ontology 101 - Kendall & McGuiness
Ontology 101 - Kendall & McGuiness
Ontology 101 - Kendall & McGuiness
Ontology 101 - Kendall & McGuiness
Ontology 101 - Kendall & McGuiness
Ontology 101 - Kendall & McGuiness
Ontology 101 - Kendall & McGuiness
Ontology 101 - Kendall & McGuiness
Ontology 101 - Kendall & McGuiness

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Ontology 101 - Kendall & McGuiness

  • 1. + Ontology 101: An Introduction to Knowledge Representation, the Web Ontology Language (OWL) & Ontology Development Elisa Kendall, Thematix & Deborah McGuinness, RPI / McGuinness Associates 6 June 2011
  • 2. + Content management Mars was photographed by the Hubble Space Telescope in August 2003 as 2 2 the planet passed closer to Earth than it had in nearly 60,000 years. Image Credit: NASA, J. Bell (Cornell U.) and M. Wolff (SSI) A sunset on Mars creates a glow due to the presence of tiny dust particles in the atmosphere. This photo is a combination of four images taken by Mars Pathfinder, which landed on Mars in 1997. Image credit: NASA/JPL Recent images from instruments on board the Mars Reconnaissance Orbiter take much more detailed, narrower views of specific features of the Martian surface. Image credit: NASA/JPL The Planetary Data Store (PDS) is a distributed repository of 40+ years’ imagery & data taken by a range of instruments on many diverse missions, available for scientific research. Copyright © 2011 Thematix & McGuinness Associates
  • 3. + Smart search 3 3 Provenance/sources for tracking family members in the 19th century include early census data (often error prone), military records, passenger & immigration lists, online documents (e.g., county histories, church histories, etc.) • Historical/forensic research requires cross-domain search of a wide variety of resources within a given geo-spatial/temporal context • Similar capabilities are essential for business intelligence, law enforcement, government applications – all require terminology reconciliation Copyright © 2011 Thematix & McGuinness Associates
  • 4. + Merchandising 4 4 Attribute 1 Attribute 2 Leg Room Attribute 4 Attribute 5 Attribute 5 On-Off Access “Comfort- Attribute 7 Attribute 8 Oriented” Attribute 9 Flight Media Options Attribute 11 Attribute 12 Meal Options Attribute 14 Attribute 15 Time of Day Attribute 17 Attribute 18 Distance to Gate Attribute 20 Attribute 21 Flight Characteristics Attribute N … Copyright © 2011 Thematix & McGuinness Associates
  • 5. + Search Engine Optimization 5 5  Use search engines as a front door to transaction  Dramatically increase relevance by inclusion of more meaningful, synonymic tags  Enrich search results, adding ratings, video, prices, avails, amenities, locality etc. Copyright © 2011 Thematix & McGuinness Associates
  • 6. + Tutorial Outline 6 6  Introduction to Knowledge Representation  OWL Basics  Tools & Applications Copyright © 2011 Thematix & McGuinness Associates
  • 7. + Part 1: Introduction to Knowledge Representation 7 7  A little background and a few definitions  Layers of abstraction & conceptual modeling  Classifying ontologies  A little methodology Copyright © 2011 Thematix & McGuinness Associates
  • 8. + Definitions 8 8  An ontology is a specification of a conceptualization. – Tom Gruber  Knowledge engineering is the application of logic and ontology to the task of building computable models of some domain for some purpose. – John Sowa  Artificial Intelligence can be viewed as the study of intelligent behavior achieved through computational means. Knowledge Representation then is the part of AI that is concerned with how an agent uses what it knows in deciding what to do. – Brachman and Levesque, KR&R  Knowledge representation means that knowledge is formalized in a symbolic form, that is, to find a symbolic expression that can be interpreted. – Klein and Methlie  The task of classifying all the words of language, or what's the same thing, all the ideas that seek expression, is the most stupendous of logical tasks. Anybody but the most accomplished logician must break down in it utterly; and even for the strongest man, it is the severest possible tax on the logical equipment and faculty. – Charles Sanders Peirce, letter to editor B. E. Smith of the Century Dictionary Copyright © 2011 Thematix & McGuinness Associates
  • 9. + What is an Ontology? An ontology specifies a rich description of the 9 9  Terminology, concepts, nomenclature  Properties explicitly defining concepts  Relations among concepts (hierarchical and lattice)  Rules distinguishing concepts, refining definitions and relations (constraints, restrictions, regular expressions) relevant to a particular domain or area of interest. Copyright © 2011 Thematix & McGuinness Associates
  • 10. + Logic and Ontology 10 10  Predicate logic is harder to read than the original English, but is more precise: Every semi-trailer truck has at least 3 axles. (∀ x)(((SemiTrailerTruck(x) ∧ (∃ y)(SemiTrailer(y) ∧ (hasPart (x,y))) ∧ (SemiTrailerTruck(x) ∧ (∃ z)(TractorUnit(z) ∧ (hasPart (x,z)))) ⊃ (∃ s)(set(s) ∧ (count(s,(≥3)) ∧ (∀ w)(member(w,s) ⊃ (Axle(w) ∧ hasPart(x,w))) )).  Logic is a simple language with few basic symbols.  The level of detail depends on the choice of predicates – these predicates represent an ontology of the relevant concepts in the domain.  Different choices of predicates represent different ontological commitments. * Derived from Knowledge Representation: Logical, Philosophical, and Computational Foundations, John F. Sowa, Brooks/Cole, Pacific Grove, CA, 2000. Copyright © 2011 Thematix & McGuinness Associates
  • 11. + Ontology-based Technologies 11 11  Ontologies provide a common vocabulary for use by independently developed resources, processes, services  Agreements among organizations sharing common services can be made with regard to their usage; the meaning of relevant concepts can be expressed unambiguously  By composing / mapping ontologies and mediating terminology across participating events, resources and services, independently-developed services can work together to share information and processes consistently, accurately, and completely  Ontologies also ensure  Valid conversations among agents to collect, process, fuse, and exchange information  Accurate searching by ensuring context using concept definitions and relations instead of/in addition to statistical relevance of keywords Copyright © 2011 Thematix & McGuinness Associates
  • 12. + Features of KR languages  Vocabulary – a collection of symbols 12 12  Domain-independent logical symbols (e.g., ∀ or ⊃)  Domain-dependent constants, identifying individuals, properties, or relations in the application domain or universe of discourse  Variables, whose range is governed by quantifiers  Punctuation that separates or groups other symbols  Syntax –  formation rules that determine how symbols can be combined in well- formed expressions  rules may be stated in a linear grammar, graph grammar, or independent abstract syntax  Semantics –  a theory of reference that determines how the constants and variables are associated with things in the universe of discourse  a theory of truth that distinguishes true statements from false statements  Rules of Inference –  rules that determine how one pattern can be inferred from another  if the logic is sound, the rules of inference must preserve truth as determined by the semantics Copyright © 2011 Thematix & McGuinness Associates
  • 13. + Logic Logics vary from classical FOL along six dimensions: 13 13  Syntax  Subsets limit permissible operators or combinations, e.g., propositional logic (without quantifiers), Horn-clause (excludes disjunctions in conclusions, such as Prolog), terminological or definitional logics (containing additional restrictions, e.g., description logics)  Proof Theory restricts or extends permissible proofs: • Intuitionistic logic and relevance logic rule out certain extraneous information • Non-monotonic logics allow introduction of default assumptions • Access-limited logic restricts the number of times a proposition can be used in a proof; Linear logic allows a proposition to be used only once • Modal logic incorporates modal auxiliaries (◊p means p is possibly true; p means p is  necessarily true), temporal logic extends model logic to include always, sometimes • Intensional logics express concepts such as need, ought, hope, fear, wish, believe, know, expect, and intend  Model Theory modifies the truth value of statements in terms of some model of the world: classical FOL is two-valued; a three-valued logic introduces unknowns; fuzzy logic uses the same notation as FOL but with an infinite range of certainty factors (1.0 to 0.0)  Ontology – frameworks may include support for built-in components, such as set theory or time  Meta-language – language for encoding information about objects Copyright © 2011 Thematix & McGuinness Associates
  • 14. + Description logic  A family of logic-based Knowledge Representation formalisms 14 14  Descendants of semantic networks and KL-ONE  Describe domain in terms of concepts (classes), roles (relationships), and individuals (instances)  Distinguished by  Formal semantics • Decidable fragments of FOL • Closely related to Propositional, Modal, and Dynamic Logics  Provision of inference services • Sound and complete decision procedures for key problems • Implemented systems (highly optimized)  Applications include  Configuration – product configurators, consistency checking, constraint propagation, first significant industrial application (e.g., CLASSIC)  Ontologies – ontology engineering (design, maintenance, integration), reasoning with ontology-based mark-up, service description and discovery  Databases – consistency of conceptual schemata, schema integration, query subsumption (w.r.t. conceptual schemata) Copyright © 2011 Thematix & McGuinness Associates
  • 15. + Knowledge bases, databases, & ontology 15 15  An ontology is a conceptual model of some aspect of a particular universe of discourse (or of a domain of discourse)  Typically, ontologies contain only “rarified” or “special” individuals, metadata, representing elemental concepts critical to the domain  A knowledge base is a persistent repository for  Ontology & metadata representing individuals, facts, & rules about how they can be combined or relate to one another  Metadata, facts & rules only – in some applications and frameworks the ontology is separately maintained  Most inference engines require in-memory deductive databases for efficient reasoning (including commercially available reasoners)  A knowledge base may be implemented in a physical, external database, such as a relational database, but reasoning is typically done on a subset (partition) of that knowledge base in memory Copyright © 2011 Thematix & McGuinness Associates
  • 16. + Reasoning & Truth Maintenance 16 16  Reasoning is the mechanism by which the assertions made in an ontology and related knowledge base are evaluated by an inference engine.  In classical logic, the validity of a particular conclusion is retained even if new information is received.  This may change if some of the preconditions are actually hypothetical assumptions invalidated by the new information.  The same idea applies for arbitrary actions – new information can make preconditions invalid.  Generally, there are two issues that a reasoner must address:  If some conclusion is invalid, which other conclusions are also invalid?  If some action cannot be performed, which others are at risk?  The “housekeeping” associated with tracking the threads that support answering these questions is called truth maintenance. Copyright © 2011 Thematix & McGuinness Associates
  • 17. + Negation  If all new information is “positive”, then all prior conclusions should 17 17 remain valid.  Problems arise if new information negates a prior assumption, causing it to be withdrawn.  What does it mean to negate (withdraw) an assumption?  Conclusive information is not available?  The assumption cannot be proven?  The assumption is not provable using certain methods?  The assumption is not provable given a fixed quantity of time?  The answers to these questions can result in different definitions of negation and differing interpretations by non-monotonic reasoners.  Solutions include chronological and “intelligent” backtracking algorithms, heuristics, circumscription algorithms, justification or assumption based retraction, and so forth, depending on the reasoner and methods used for truth maintenance.  Reasoning efficiency is dependent, in part, on the algorithms applied for truth maintenance. Copyright © 2011 Thematix & McGuinness Associates
  • 18. + Explanations and Proofs 18 18  When a reasoner draws a particular conclusion, many users and applications want to understand why?  Primary motivations include interoperability, reuse, and trust  Understanding the provenance of the information and results is crucial, especially when web-based information is involved  What information sources were used (source)  How recently they were updated (currency)  How reliable these sources are (authoritativeness)  Was the information directly available or derived, and if derived, how (method of reasoning)  Methods used to explain why a reasoner reached a particular conclusion include explanation generation and proof specification Copyright © 2011 Thematix & McGuinness Associates
  • 19. + Abstraction Layers • Identify subject areas Contextual 19 19 Ontology Conceptual • Define the meaning of things in the organization • Describe the logical representation of properties Ontology, Conceptual ER, Conceptual Logical Business Process … • Describe the physical means by which data is stored ER, Relational, XML Schema Physical XML, source code, scripting languages, • Represent the coding language on a specific development platform stored procedures… Definition Physical KBs, • Hold the values of the properties applied to the data in a schema databases, asset repositories… Instance *Layering diagram courtesy Kenn Hussey  Knowledge Representation / Management for Large Scale Applications  Provide broad metadata, process, service & asset management facilities (including feedback/lessons learned…)  Enable rich cross-domain, cross-process, cross organizational modeling supported by mapping & transformation services to provide maximum flexibility, interoperability  Leverage standards and best practices in information architecture, metadata modeling, management, registration, and governance, and asset management & registration  Provide incremental reasoning capabilities for model validation, transformation services  Repeatable, reusable, interoperable Copyright © 2011 Thematix & McGuinness Associates
  • 20. + Hypothetical Conceptual Model “EU-Rent” 20 20 Produced using Embarcadero EA/Studio Business Modeler Edition, courtesy Kenn Hussey Copyright © 2011 Thematix & McGuinness Associates
  • 21. + Hypothetical Logical Model “EU-Rent” 21 21 Produced using Embarcadero ER/Studio, courtesy Kenn Hussey Copyright © 2011 Thematix & McGuinness Associates
  • 22. + Hypothetical Physical Model “EU-Rent” 22 22 Produced using Embarcadero ER/Studio, courtesy Kenn Hussey Copyright © 2011 Thematix & McGuinness Associates
  • 23. + Classifying ontologies 23 23 Classification techniques are as diverse as conceptual models; and generally include understanding KR System  Level of Expressivity  Level of Complexity / Structure OO Software Model  Granularity Entity – Relationship  Target Usage, Relevance Model  Amount of Automation, Reasoning Requirements Concept Map  Prescriptive vs. Descriptive / Reliability / Level Topic Map of Authoritativeness Level of Expressivity  Design Methodology Database Schema  Governance XML Schema  Vocabulary Management, Metrics Hierarchical Taxonomy Simple Taxonomy Glossary Level of Complexity Copyright © 2011 Thematix & McGuinness Associates
  • 24. + Framework of dimensions 24 24  Semantic Dimensions  Expressiveness: represents how well a KR language addresses increasingly complex semantics  Structure: represents how well an ontology encodes semantics, with the same or less expressivity than the KR language  Granularity: represents the level of detail specified in an ontology  Pragmatic Dimensions  Intended use: the original use case(es), or purpose for developing a particular ontology  Automated reasoning: the extent to which the ontology is designed to be used for automated reasoning  Prescriptive vs. Descriptive: the extent to which an ontology was intended to be used for descriptive purposes vs. normative prescriptive use (i.e., with high degree of concern for correctness) Reference: http://ontolog.cim3.net/cgi-bin/wiki.pl?OntologySummit2007 Copyright © 2011 Thematix & McGuinness Associates
  • 25. + Model dynamics 25 25 Model centric perspectives characterize the ontologies themselves and are concerned with their structure, formalism and dynamics. Perspective One Extreme Other Extreme Level of Least authoritative, broader Most authoritative, narrower, Authoritativeness shallowly defined ontologies more deeply defined ontologies Source of Passive (Transcendent) - Active (Immanent) - Structure Structure Structure originates outside emerges from data or behavior the system Degree of Informal or primarily Formal, having rigorously Formality taxonomic defined types, relations, and theories or axioms Model Dynamics Read-only, ontologies are Volatile, ontologies are fluid & static changing Instance Read-only, resource instances Volatile, resource instances Dynamics are static change continuously Copyright © 2011 Thematix & McGuinness Associates
  • 26. + Application characteristics 26 26 Application centric perspectives are concerned with how applications use and manipulate ontologies. Perspective One Extreme Other Extreme Control/Degree Externally focused, public Internally focused, private of Manageability (little or no control) (full control) Application Static (with periodic updates) Dynamic Changeability Coupling Loosely-coupled Tightly-coupled Integration Focus Information integration Application integration Lifecycle Usage Design Time Run Time Copyright © 2011 Thematix & McGuinness Associates
  • 27. + Language evaluation 27 27 KR Expressivity Requirements Reasoning Requirements Classes Exceptions Slots/Attributes Automatic Classification Metaclasses Number Restrictions Complex Class Extensions (union, intersection) Inheritance Subsumption Hierarchies Monotonic, Nonmonotonic Value Restrictions Simple, Multiple Behaviors, Procedural Definitions, Methods Procedural Support, Execution Relations / Functions Slots/Attributes Subsumption Hierarchies Constraint Checking, Deduction Built-in Functions, Equations, Formulae Instances / Individuals / Facts Axioms Reasoning with Rules Production Rules (forward and backward chaining) * Derived from “Evaluating Knowledge Representation and Reasoning Capabilities of Ontology Specification Languages”, Oscar Corcho and Asuncion Gomez-Perez, January 2002 Copyright © 2011 Thematix & McGuinness Associates
  • 28. + Considerations  Intended use of ontologies, including domain requirements (e.g., 28 28 scientific and engineering apps require formulas, units of measure, computations that may be challenging to represent)  Intended use of KRSs that implement them, including reasoning requirements, questions to be answered  For distributed environments, the number and kinds of resources, processes, services requiring ontologies – how distributed, how unique, developed collaboratively or independently, dynamic community participation or static  What kinds of transformations are required among processes, resources, services to support semantic mediation  Ontology and KRS alignment / de-confliction / ambiguity resolution requirements  Ontology and KRS composition requirements, dynamic vs. static composition, in what environment and under what constraints  Performance, sizing, timing requirements of target environment Copyright © 2011 Thematix & McGuinness Associates
  • 29. + A Little Methodology … 29 29  Requirements, domain & use case analysis are critical  Develop initial source/reference material  Focus on system or application requirements  Iterative development starting with a “thread” that covers basic capabilities can ground the work and prioritize decisions  Need to understand and communicate  Architectural trade-offs, cost & technical benefits  The nature of the information & kinds of questions that need to be answered drive the architecture, approach, and ontology scoping and design  Reuse standards and well-tested, available ontologies whenever possible Copyright © 2011 Thematix & McGuinness Associates
  • 30. + What to look for  A controlled vocabulary 30 30 FAA & IATA airport codes, ACRISS car codes, …  A hierarchical or taxonomic structure (for query expansion) Vehicle, Ground-based Vehicle, Wheeled Vehicle, Powered Vehicle, Automobile, Sedan…  Knowledge supporting structured queries Find all available hybrid SUVs or hybrid sedans that can seat four adults within a reasonable taxi ride of Planet Hollywood, Las Vegas for 3-4 days the week of June 20th  Efficient inference (i.e., limited expressive power) vs. increased expressivity (potentially expensive or resource bounded computation)  Custom reasoning for temporal relations, geospatial, dynamic algorithm / equation evaluation, process-specific, conditional operations  Computational tractability Copyright © 2011 Thematix & McGuinness Associates
  • 31. + Start with canonical definitions  General concepts as well as domain-specific knowledge 31 31  Basic starting point – cross-domain definitions  Namespace definitions, metadata, naming conventions, governance policies  Commonly used structures & vocabularies, such as domain-specific vocabulary & messaging standards, international country & language codes (ISO), national postal addressing or other government standards, industry best practices  Common metadata for ontology & schema management (e.g., Dublin Core, for documents & models, ISO 1087 for synonyms & similar relations, MIME media types for images & multimedia, etc.)  Domain vocabularies must be prioritized, selected based on business requirements, clear ROI  Common early targets include  Smart search (pull); customer experience & cross-sell / upsell (push)  Richer interoperability among trading partners  Service registration, description, discovery & management  Asset/artifact repository search & retrieval  Automated verification Copyright © 2011 Thematix & McGuinness Associates
  • 32. + IDEF5 (Integrated Definition Methods) Ontology Capture Method Analysis 32 32  Layout a high-level architecture for ontology elements  Identify the relationships among elements – roles, domain, interface, process, utility  For each ontology element  Describe its domain and scope, how it will be used  Identify example questions and anticipated/sample answers for the application(s) it will support  Identify key stakeholders, ownership, maintenance, resources for instance knowledge  Describe anticipated reuse/evolution path  Identify critical standards, resources that it must interoperate with, dependencies  Resources  http://www.idef.com/IDEF5/html  http://www.kbsi.com/technology/methods/sbont.htm Copyright © 2011 Thematix & McGuinness Associates
  • 33. + Principles & Methods for Terminology Work (ISO 704) 33 33  Methodology for describing concepts & terms  Uses ISO 1087 for terminology  Uses ISO 860 for terminology “harmonization” (alignment) methods  Basis for typical methods used for taxonomy development today  Describes how to flesh out definitions  Recommendations strategies for relating terms to one another using standard vocabulary  ISO 1087 – great resource for language to describe kinds of relationships, acronyms & other designations, preferred vs. deprecated terms, etc.  ISO 860 augments this with recommendations for vocabulary comparison Copyright © 2011 Thematix & McGuinness Associates
  • 34. + Part 2: OWL Basics  A quick intro to RDF 34 34  Basic OWL constructs: classes, properties & slots  Inheritance & constraints  Class & slot inheritance  Property, slot & slot value constraints  Individuals & literals  Disjoint & equivalence relations  New features of OWL 2  A few rules of thumb  Depth vs. breadth, naming, synonymous terms  Class vs. property value, class vs. individual  Inverse properties  Limiting scope Copyright © 2011 Thematix & McGuinness Associates
  • 35. + Semantic Web 35 35 "The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation." -- Tim Berners-Lee Copyright © 2011 Thematix & McGuinness Associates
  • 36. + Guiding Principles 36 36  Historically, knowledge representation and reasoning systems have operated under closed world assumptions  Uncertainty is magnified under open-world, “wild, wild web” conditions, making reasoning much more difficult  Semantic web languages are designed to support less certainty, to provide “better” search results, informed answers to questions, not absolutes  Because they are based on XML, such languages can assist businesses in leveraging existing investment in mark-up, content, and data  To augment business intelligence/analysis and knowledge mining  To support knowledge sharing and collaboration, augment enterprise information integration  Enrich web services and other applications  Support policy-based applications and ensure compliance with policy at a lower cost with higher potential ROI than traditional computing methods Copyright © 2011 Thematix & McGuinness Associates
  • 37. + Resource Description Framework (RDF) 37 37  Describes relationships  Uses URIs used for naming  Language has  graph based model  RDF/XML serialization (exchange syntax)  other presentation syntaxes (N3, Turtle, …)  Specification, W3C presentations, tools are available at  Semantic Web: http://www.w3.org/standards/semanticweb/  Linked Data: http://www.w3.org/standards/semanticweb/data  RDF: http://www.w3.org/standards/techs/rdf#w3c_all  New RDF “next steps” revision working group under way to make specific enhancements, “fixes” to the standard  RDF Working Group: http://www.w3.org/2011/rdf-wg/wiki/Main_Page Copyright © 2011 Thematix & McGuinness Associates
  • 38. + RDF Notation Options 38 38 Subject http://semtech2011.semanticweb.com/travel.owl#Conference Predicate Graph: http://semtech2011.semanticweb.com/travel.owl#heldAt http://semtech2011.semanticweb.com/travel.owl#HiltonSFUnionSquare Object <rdf:Description rdf:ID=“Conference"/> XML/RDF: <semtech:heldAt rdf:resource="#HiltonSFUnionSquare"/> </rdf:Description> N3: semtech:Conference semtech:heldAt semtech:HiltonSFUnionSquare . Copyright © 2011 Thematix & McGuinness Associates
  • 39. + RDF Schema (RDFS)  An RDF vocabulary that provides for identifying: 39 39  classes,  subsumption (inheritance) relations for classes,  subsumption (inheritance) relations for properties,  domain and range for properties Copyright © 2011 Thematix & McGuinness Associates
  • 40. + RDF Schema (RDFS) 40 40 semtech:Hotel rdfs:subClassOf Graph: rdf:type rdfs:Class semtech:ConferenceHotel rdf:type rdf:type semtech:HiltonSFUnionSquare <rdf:Description rdf:ID="Hotel"> XML/RDF: <rdf:type rdf:resource="http://www.w3.org/2000/01/rdf-schema#Class"/> </rdf:Description> <rdfs:Class rdf:ID=“ConferenceHotel"> <rdfs:subClassOf rdf:resource="#Hotel"/> </rdfs:Class> <semtech:ConferenceHotel rdf:ID=“HiltonSFUnionSquare“/> Copyright © 2011 Thematix & McGuinness Associates
  • 41. + The Web Ontology Language (OWL)  Two languages emerged in parallel to address semantic web 41 41 requirements  DAML-ONT, supported by the DARPA/DAML program  OIL (Ontology Inference Layer) developed by EU & US researchers  Merged DAML+OIL was submitted to the W3C 2002, formed the basis for the WebOnt Working Group  OWL extends RDF Schema  Has an RDFS based syntax and reuses some RDF vocabulary (e.g., subClassOf, domain, range)  Adds rich primitives and redefines others (transitivity, inverse, cardinality constraints, complex class definitions)  Describes the structure of a domain in terms of classes and properties  Uses RDFS for class/property membership assertions (ground facts), XML Schema Datatypes  OWL specifications became W3C recommendations in February 2004  OWL 2 specifications was adopted in October 2009 Copyright © 2011 Thematix & McGuinness Associates
  • 42. + General nature of descriptions 42 42 a WINE a LIQUID General Categories a POTABLE grape: chardonnay, ... [>= 1] sugar-content: dry, sweet, off-dry Structured color: red, white, rose Components price: a PRICE winery: a WINERY grape dictates color (modulo skin) Interconnections harvest time and sugar are related Between Parts Copyright © 2011 Thematix & McGuinness Associates
  • 43. + General nature of descriptions 43 43 Class a WINE Superclass a LIQUID General Categories a POTABLE Number / Card Restrictions grape: chardonnay, ... [>= 1] sugar-content: dry, sweet, off-dry Structured Roles / color: red, white, rose Components Properties price: a PRICE winery: a WINERY Value Restrictions grape dictates color (modulo skin) Interconnections harvest time and sugar are related Between Parts Copyright © 2011 Thematix & McGuinness Associates
  • 44. + Description development 44 44  Define domain terms and inter-relationships  Define concepts in the domain (classes, nouns)  Identify subclass/superclass relationships  Identify attributes/properties/slots (verbs)  Identify any general properties (relations, functions, verbs)  Restrict slot values  Define a representative set of individuals – for testing & evaluation, prototype development  Define individuals (instances)  Define relationships between individuals (filling in slots) Copyright © 2011 Thematix & McGuinness Associates
  • 45. + Classes & class hierarchy 45 45  A class is a concept in the domain  Vintage – a wine made from grapes grown in a specified year  A class of properties (flavor, body, color, sugar…)  A class is a collection of elements with similar properties  White wine – wines made from white grapes  White table wine – wines made from white grapes that are not appellations or regional (not “quality wine” in the EU)  A class contains necessary conditions for membership (specific varietal, field, micro-climate, date picked, date bottled)  Instances of classes  Andrew Murray 2005 Late Harvest Viognier  Robert M. Parker, Jr,’s Wine Advocate review, dated February 28, 2002  Los Olivos, Santa Barbara County, California, USA Copyright © 2011 Thematix & McGuinness Associates
  • 46. + Class inheritance 46 46  Classes are organized into subclass-superclass (or generalization-specialization) hierarchies  True subclass relationships are the basis of a formal is-a hierarchy Classes are “is-a” related if an instance of the subclass is an instance of the superclass  Classes may be viewed as sets  Subclasses of a class are comprised of a subset of the superset  Examples  RedWine is a subclass of Wine Every red wine is a wine or every instance of a red wine (e.g., Marietta Old Vines Red) is an instance of wine  NapaValleyWine is a subclass of CaliforniaWine Every wine from Napa Valley is a wine from California Copyright © 2011 Thematix & McGuinness Associates
  • 47. + Levels in the class hierarchy 47 47  Different modes of development  Top-down - define the most general concepts first and then specialize them  Bottom-up - define the most specific concepts and then organize them into more general classes  Combination (typical – breadth at the top level and depth along a few branches to test design)  Class inheritance is transitive  A is a subclass of B (white wine, dessert wine are subclasses of wine)  B is a subclass of C (viognier is a subclass of white wine, late harvest wine is a subclass of dessert wine)  therefore A is a subclass of C (late harvest viognier is a subclass of white wine, dessert wine, & wine) Copyright © 2011 Thematix & McGuinness Associates
  • 48. + Classes in OWL 2 48 48  6 kinds of class expressions in OWL  5 anonymous  Definitions can be nested using set-theoretic constructs Copyright © 2011 Thematix & McGuinness Associates
  • 49. + Properties & slots 49 49  Slots in a class definition describe attributes of members of a class Each wine will have color, sugar content, flavor, body, etc.  Types of properties  “intrinsic” properties: flavor and color of wine  “extrinsic” properties: name and price of wine  parts: ingredients in a recipe  relations to other objects: producer of wine (winery)  Data and object properties  simple (datatype) contain primitive values (strings, numbers)  complex properties may contain other objects (e.g., a winery instance) Copyright © 2011 Thematix & McGuinness Associates
  • 50. + Properties in OWL 2 50 50 Copyright © 2011 Thematix & McGuinness Associates
  • 51. + Class & slot inheritance 51 51  A subclass inherits all the slots from its super class If a wine has a name and flavor, a red wine also has a name and flavor  If a class has multiple super classes, it inherits slots and restrictions from all of them Latin is both an individual language and an ancient language. It inherits “has attested literature: true” from ancient language and “has indigenous name: lingua latina” from individual language Copyright © 2011 Thematix & McGuinness Associates
  • 52. + Property (slot) constraints / restrictions  Number restrictions describe or limit the number of possible values a particular property can have 52 52  A language must be associated with at least one English name and at least one French name  A language may be associated with zero or more Indigenous names  Cardinality – similar meaning to classical set theory, measures the number of elements in the set (restriction class)  Cardinality – cardinality N means the class defined by the property restriction must have exactly N values (individual or literal values)  Minimum cardinality - 1 means that there must be at least one value (required), 0 means that the value is optional  Maximum cardinality - 1 means that there can be at most one value (single-valued), N means that there can be up to N values (N > 1, multi- valued) Copyright © 2011 Thematix & McGuinness Associates
  • 53. + Slot value constraints 53 53  Slot value type – defines the set of possible values for the property  String: a string of characters (“McGinley Vineyard”)  Number: an integer or a float (3.5 acres)  Boolean: a true/false flag  Enumerated type: a list of allowed values (red, white, rose)  Filler: a single value (e.g., the color slot for a RedWine must be filled with the single value “red”)  Object type – a class defined in an ontology (e.g., Winery is the value restriction on the hasMaker slot on the class Wine) Copyright © 2011 Thematix & McGuinness Associates
  • 54. + Domain & range properties 54 54  In OWL and many other KR languages, relations (properties, slots) are strictly binary  The domain & range represent the source & target arguments, respectively, for the property  Domain of a slot – the class (or classes) that may have the slot − Wine is the domain of the slot hasWineColor  Range of a slot – the class (or classes) to which slot values belong − everything that fills the hasWineColor slot is an instance of the enumerated class {red, white, rose}  Some KR languages that inherently support n-ary relations, such as CL, do not make this distinction  More flexible, intuitively more like mathematics, where functions have ranges (or return types) but not all relations are functions  Requires additional relations to specify argument order, which can be critical for ontology alignment Copyright © 2011 Thematix & McGuinness Associates
  • 55. + Property inheritance  A subclass inherits all the slots of its superclass(es) 55 55  A subclass can add constraints to “narrow” the set of allowed values  Make the cardinality range smaller  Replace a class in the range with a subclass Copyright © 2011 Thematix & McGuinness Associates
  • 56. + Individuals 56 56  An Individual (instance, object in other paradigms)  Any class that an individual is a member of, or is an individual of, is a type of the individual  Any superclass of a class is an ancestor (or type) of the individual  Specify slot values for the individual  Slot values should conform to the constraints such as range, value type, cardinality restrictions, etc. Copyright © 2011 Thematix & McGuinness Associates
  • 57. + OWL Individuals 57 57 Graco Garmin Chevrolet Sony BMW Copyright © 2011 Thematix & McGuinness Associates
  • 58. + OWL Statements 58 58 Graco Garmin Chevrolet Sony BMW a Mini Cooper a Camaro LT Convertible Copyright © 2011 Thematix & McGuinness Associates
  • 59. + OWL ObjectProperty 59 59 Graco Garmin Chevrolet Sony <owl:ObjectProperty rdf:ID="builtBy"> BMW <rdfs:range rdf:resource="#Enterprise"/> <rdfs:domain rdf:resource="#DurableGood"/> <owl:inverseOf rdf:resource="#hasBuilt"/> </owl:ObjectProperty> a Mini Cooper a Camaro LT Convertible Copyright © 2011 Thematix & McGuinness Associates
  • 60. + OWL ObjectProperty 60 60 range Graco Garmin Chevrolet Sony BMW <owl:ObjectProperty rdf:ID="builtBy"> <rdfs:range rdf:resource="#Enterprise"/> <rdfs:domain rdf:resource="#DurableGood"/> <owl:inverseOf rdf:resource="#hasBuilt"/> </owl:ObjectProperty> a Mini Cooper a Camaro LT Convertible Copyright © 2011 Thematix & McGuinness Associates
  • 61. + OWL ObjectProperty 61 61 range Graco Garmin Chevrolet Sony <owl:ObjectProperty rdf:ID="builtBy"> BMW <rdfs:range rdf:resource="#Enterprise"/> <rdfs:domain rdf:resource="#DurableGood"/> <owl:inverseOf rdf:resource="#hasBuilt"/> </owl:ObjectProperty> a Mini domain Cooper a Camaro LT Convertible Copyright © 2011 Thematix & McGuinness Associates
  • 62. + Inverse Properties 62 62 domain Graco Garmin Sony Chevrolet <owl:ObjectProperty rdf:ID="hasBuilt"> BMW <rdfs:range rdf:resource="#DurableGood"/> <rdfs:domain rdf:resource="#Enterprise"/> <owl:inverseOf rdf:resource="#builtBy"/> </owl:ObjectProperty> a Mini range Cooper a Camaro LT Convertible Copyright © 2011 Thematix & McGuinness Associates
  • 63. + OWL Class 63 63 Graco Garmin Chevrolet <owl:Class Sony rdf:ID="ManufacturingEnterprise"/> BMW <owl:Class rdf:ID="DiscreteManufacturingEnterprise"> <rdfs:subClassOf> <owl:Class rdf:about="#ManufacturingEnterprise"/> </rdfs:subClassOf> </owl:Class> Copyright © 2011 Thematix & McGuinness Associates
  • 64. + Class Axioms 64 64 A ⊆ B where B  Subsumption (necessary) B  is a class description partial or primitive class A  Definition (necessary and sufficient)  C ≡ D where D D is a class description complete or defined class C Courtesy Evan Wallace, NIST Copyright © 2011 Thematix & McGuinness Associates
  • 65. + Meta-Properties – Global Cardinality Restriction 65 65 Range  Functional Domain Range  Inverse Functional Courtesy Evan Wallace, NIST Domain Copyright © 2011 Thematix & McGuinness Associates
  • 66. + Class Descriptions – Property Restriction 66 66 property P Individual of Class C  Quantified property restriction (type)  Universally quantified – allValuesFrom  Existentially quantified - someValuesFrom  hasValue property restriction (value)  Property cardinality restriction (# of values) Courtesy Evan Wallace, NIST Copyright © 2011 Thematix & McGuinness Associates
  • 67. + Disjoint classes A 67 67 B  Classes are disjoint if they cannot have common instances  Disjoint classes cannot have any common subclasses  If winery and wine are disjoint, then there is no instance that is both a winery and a wine; there is no class that is both a subclass of winery and a subclass of wine  Disjointness is often used to aid consistency checking  Disjointness is also helpful in teasing out subtle distinctions among classes across multiple ontologies  Equivalence is also often used to identify the same concepts across ontologies that may be named differently, or to name classes defined through class axioms Copyright © 2011 Thematix & McGuinness Associates
  • 68. + New features in OWL 2 68 68  New property constructs  Reflexive, irreflexive, & asymmetric properties  Property chains  Disjoint properties  Keys  Self-reflexive properties  Qualified cardinality restrictions  Negative property assertions  New class axioms  Disjoint unions  Disjoint classes Copyright © 2011 Thematix & McGuinness Associates
  • 69. + More new features  Extended datatype handling 69 69  Better support for XML Schema Datatypes  New datatypes for OWL specifically (owl:real, owl:rational, rdf:PlainLiteral)  Custom datatype definition & use of xsd facets / datatype restrictions  New data range restrictions  Intersections, unions, complements  Support for punning  Limited support for a particular element to be defined as both a class & individual, for example  Improved support for annotations (e.g., metadata about ontologies, provenance, transformation detail, etc.)  See http://www.w3.org/TR/owl2-new-features/ & http://www.w3.org/TR/owl2-quick-reference/ for detail  Current work specifically focused on provenance: http://www.w3.org/2011/prov/wiki/Main_Page Copyright © 2011 Thematix & McGuinness Associates
  • 70. + Rules of Thumb 70 70  Cycles are common in many KR systems, though rarely “a good thing”  Cycles are disallowed by some tools because they prohibit “code generation”, export -- including RDF/OWL  Classes A, B, and C have equivalent sets of instances  By many definitions, A, B, and C are equivalent  Use owl:equivalentClass instead of creating cycles Copyright © 2011 Thematix & McGuinness Associates
  • 71. + Siblings in the class hierarchy 71 71  All siblings should be specified at roughly the same level of generality  Compare to section and subsections in a book Copyright © 2011 Thematix & McGuinness Associates
  • 72. + Class specification 72 72  If a class has only one child, there may be a modeling problem – often a sign that a definition is incomplete  If the only Red Burgundy we have is Côtes d’Or, why introduce the subclass?  Subclasses of a class usually have  Additional properties  Additional slot restrictions  Participate in different relationships  Compare to bullets in a bulleted list Copyright © 2011 Thematix & McGuinness Associates
  • 73. +Creating levels & subclasses 73 73  If a class has a large number of subclasses, it may be useful to define intermediate levels  For example, in the domain of wines, there are natural groupings around wine color  However, if no natural classification exists, the long list may be appropriate Copyright © 2011 Thematix & McGuinness Associates
  • 74. + Inheritance, naming, synonyms 74 74  A “wine” is not a subclass of “wines”  A particular vintage should be classified as an instance of the class Wines Class  Class names should be either  all singular  all plural instance-of  Synonym names for the same concept are not Instance different classes MariettaOldVinesRed  Many systems, metadata standards support synonymous terms as part of a class definition  OWL allows defining necessary and sufficiency condition definitions thereby allowing synonym definitions to be “first class” terms Copyright © 2011 Thematix & McGuinness Associates
  • 75. + Class vs. property value 75 75  Do concepts with different slot values become restrictions for different slots?  How important is the distinction for the domain?  Class definitions for most domains should be fairly stable – i.e., they should not change frequently once the definitions are established and individuals created Copyright © 2011 Thematix & McGuinness Associates
  • 76. + Class vs. individual 76 76  Individual instances are the most specific objects in an ontology  If concepts form a natural hierarchy, represent them as classes  If they will have instances below them, represent them as classes Copyright © 2011 Thematix & McGuinness Associates
  • 77. + Limiting scope 77 77  An ontology should not contain all the possible information about the domain  No need to specialize or generalize more than the application requires  No need to include all possible properties of a class • Only the most salient properties • Only the properties that the application requires  Ontologies of wine, food, and their pairings probably will not include details such as:  Bottle size (half bottle, full bottle, magnum, …)  Label color  Wine bottle color (green, amber, …) Copyright © 2011 Thematix & McGuinness Associates
  • 78. + Part 3: Putting It All Together 78 78  Syntax Checking  Consistency Checking  Instance Data Analysis & Evaluation Services  Applications  Lighter Weight  Richer Applications  Examples – showing a combination of semantic web components in action: TW Wine Agent  Understanding, Trust & Proof: InferenceWeb  eScience Applications: Virtual Solar Terrestrial Observatory, SONet, & PopSciGrid  Leveraging Explanations in a Cognitive Assistant  Questions Copyright © 2011 Thematix & McGuinness Associates
  • 79. + Syntax checking  For RDF & OWL Ontologies 79 79  RDF syntax checking, graph visualization • W3C RDF Validator (http://www.w3.org/RDF/Validator/) • Jena API & Toolkit (http://jena.sourceforge.net/)  OWL syntax checking, OWL dialect determination • OWL Consistency Checker (http://clarkparsia.com/pellet/, also provides full OWL 2 DL reasoning capabilities) • OWL 2 Validator (Univ. of Manchester, http://owl.cs.manchester.ac.uk/validator/ ) • Protégé OWL (http://protege.stanford.edu/) • Jena API & Toolkit (http://jena.sourceforge.net/)  Every tool provides unique capabilities; sophisticated projects may require multiple approaches  Tools listed are open source, commercial options are also available Copyright © 2011 Thematix & McGuinness Associates
  • 80. + Consistency checking  Requirements are typically application specific 80 80  RDF vocabularies should be checked by an RDF validator or rule engine  Jena Semantic Web Framework - http://jena.sourceforge.net/  Pychinko, MindSwap’s Rete-based RDF-friendly rule engine – (CWM clone) http://www.mindswap.org/~katz/pychinko/  OWL Ontologies should be run through a consistency checking reasoner  Pellet (open source, originally from Mindswap, supported by Clark & Parsia, LLC) – http://clarkparsia.com/pellet/,  RacerPro – http://www.racer-systems.com/  FaCT++ – http://owl.man.ac.uk/factplusplus/  HermiT OWL Reasoner – http://www.hermit-reasoner.com/  KAON2 infrastructure for managing OWL-DL, SWRL, and F-Logic ontologies – http://kaon2.semanticweb.org/  VIStology's ConsVISor OWL Consistency checker – http://68.162.250.6:8080/consvisor/  OWL instance data may also be run through checking tools  TW Instance data evaluation - http://onto.rpi.edu/demo/oie/ Copyright © 2011 Thematix & McGuinness Associates
  • 81. + Example process 81 81 Pellet OWL DL Reasoner Valid OWL (DL consistency checked, concept satisfiability checked) TW Instance Data Evaluation Valid OWL (FOL consistency checked, deductive closure) Valid RDF/XML Documents Ontology Registry (Valid OWL Syntax Representation) & Library Application Environment Ontologies should be checked to ensure consistency, limit potential for invalid conclusions Copyright © 2011 Thematix & McGuinness Associates
  • 82. + Inconsistencies caused by distributed OWL data 82 82 Wine ontology wine:Wine rdfs:subClassOf rdfs:subClassOf wine:EarlyHarvest wine:LateHarvest owl:disjointWith A new ontology A new instance data rdfs:subClassOf rdfs:subClassOf rdf:type rdf:type wine:BadWineClass wi:BadWineInstance Case1: semantic inconsistency Case2: semantic inconsistency caused by a new class. caused by a new instance. Copyright © 2011 Thematix & McGuinness Associates
  • 83. + TW OWL instance-data evaluation 83 83  Motivation  Objective metrics are needed to evaluate if semantic web instance data is ready for practical use  Next generation Chimaera for more diverse and more instance- oriented applications  Challenges  Criteria for “ready for use” vary across applications  Existing syntax and logical-consistency checking is not enough for applications using some standard practical assumptions (e.g. closed world, unique names, …)  Solution  Extensible & customizable service-oriented architecture  Integrated environment for evaluating issues beyond standard syntactic correctness and logical consistency  See http://onto.rpi.edu/demo/oie/ Copyright © 2011 Thematix & McGuinness Associates
  • 84. + Some issues in OWL instance data 84 84 owl:cardinality owl:cardinality "1" "1" owl:onProperty owl:onProperty hasMaker hasColor rdf:type rdf:type owl:Restriction owl:Restriction rdfs:subClassOf rdfs:subClassOf Wine Wine hasColor Red W W rdf:type MPV rdf:type EPV hasColor White  Missing property value (MPV)  Excessive property value (EPV) Copyright © 2011 Thematix & McGuinness Associates
  • 85. + “Pluggable” instance evaluation services <?xml version="1.0"?> <!DOCTYPE rdf:RDF [ <!ENTITY rdf "http://www.w3.org/1999/02/22-rdf-syntax-ns#" > <!ENTITY wine "http://tw.rpi.edu/2008/04/wine-lite.owl#" > ]> 85 85 <rdf:RDF xmlns:wine = "&wine;" xmlns:rdf = "&rdf;"> <!-- A wine instance missing a value for hasMaker. Therefore, we need to report a warning indicating a missing property value. --> <wine:Zinfandel rdf:about="#W1"> <wine:hasColor rdf:resource="&wine;Red"/> <wine:hasFlavor rdf:resource="&wine;Moderate"/> <wine:hasSugar rdf:resource="&wine;Dry"/> <wine:hasBody rdf:resource="&wine;Full"/> </wine:Zinfandel> <!-- A wine instance missing a value for hasColor; however, the value for hasColor for all instances of Zinfandel has been defined in the wine ontology. Therefore, we don't need to report a warning indicating a missing property value. --> <wine:Zinfandel rdf:about="#W3"> <wine:hasFlavor rdf:resource="&wine;Moderate"/> <wine:hasSugar rdf:resource="&wine;Dry"/> <wine:hasBody rdf:resource="&wine;Full"/> <wine:hasMaker> <wine:Winery rdf:about="#Elyse" /> </wine:hasMaker> </wine:Zinfandel> </rdf:RDF> Copyright © 2011 Thematix & McGuinness Associates
  • 86. + Ontology-enhanced applications  Simple ‘ontologies’, taxonomy driven applications 86 86  Controlled, shared vocabularies (content management, faceted search)  Web site organization & navigation, expectation setting  Browsing & search engine optimization via mapping to tagged structures such as the new schema.org from Yahoo!, Google & Bing, Good Relations ontology – see http://purl.org/goodrelations/  “Smarter” search support (query expansion such as FindUR, e-Cyc; SPARQL for linked data applications)  More expressive ontologies provide more options for  Configuration, e.g., AT&T PROSE/Questar  Data Integration, e.g., Virtual Observatories (VSTO, …) Copyright © 2011 Thematix & McGuinness Associates
  • 87. Semantic Sommelier + 87 87 Semantically-enabled advisors, utilizes  ontologies  reasoning  social  mobile  provenance  context Copyright © 2011 Thematix & McGuinness Associates
  • 88. + TW Wine Agent – semantic interoperability Wine Agent receives a meal description and retrieves a 88 88 selection of matching wines available on the Web, using an ensemble of standards and tools  RDF & OWL − for encoding wine/food listings and pairing recommendations  Semantic MediaWiki − for publishing user-contributed recommendations*  Twitter and Facebook − for posting recommendations  Pellet − for deriving knowledge from wine ontology and recommendations  SPARQL − for querying wine/food listings with recommendations  Inference Web − for explaining TW Wine Agent’s intelligent behavior Copyright © 2011 Thematix & McGuinness Associates
  • 89. + Social Semantic Web data publishing 89 89 Collaborative wine recommendations  Using Semantic MediaWiki to manage users and user-contributed food/wine pairings recommendations  Using semantic form to add OWL Copyright © 2011 Thematix & McGuinness Associates instance data
  • 90. + Semantic Query: food hierarchy & recommendation 90 90 Copyright © 2011 Thematix & McGuinness Associates
  • 91. + Wine Agent processing 91 91 Given a description of a meal  Combine wine ontology and the OWL data published at Wiki  Use Pellet and SPARQL to state a premise (the meal) and query the knowledge base for a suggestion for a wine description or a set of wine instances  Use Inference Web to explain results (descriptions, instances, provenance, reasoning engines, etc.) Copyright © 2011 Thematix & McGuinness Associates
  • 92. + Wine Agent for iPhone 92 92  Client application which talks to a SW service  Make requests for dishes and wines using auto-generated interfaces  Make recommendations to the system for others Copyright © 2011 Thematix & McGuinness Associates
  • 93. + Dynamic interfaces as function of ontology 93 93 owl:Functional or rdfs:range == owl:maxCardinality == 1 owl:Class rdfs:range != owl:Class U rdfs:Literal rdfs:range == rdfs:Literal Otherwise Copyright © 2011 Thematix & McGuinness Associates
  • 94. + Descriptions 94 94  Resulting descriptions are converted to classes (for creating recommendations) or to instances (for obtaining recommendations)  When going to a class, all “Is A” statements become rdfs:subClassOf and all other properties become owl:Restrictions  Instances are realized by the reasoner to identify appropriate recommendations Copyright © 2011 Thematix & McGuinness Associates
  • 95. + Getting the Recommendation 95 95  Recommendations are made up of two classes: 1 dish, 1 wine  When the instance is classified, the agent returns the matching recommendations  Tapping a particular recommendation causes the wine agent to look for items belonging to the matching pair Copyright © 2011 Thematix & McGuinness Associates
  • 96. + Extensibility 96 96  Wine Agent Ontology is extensible  “Menu file” in RDF  Restaurants can introduce new classes and instances to provide better matches for users  Searches can be restricted to imported menus Copyright © 2011 Thematix & McGuinness Associates
  • 97. + Future Work 97 97  Group recommendations using multiple iPhones/iPod touches  Recommendations based on personal dietary needs and preferences  ‘iPellet’, an OWL Reasoner designed for iPhone, for offline reasoning Copyright © 2011 Thematix & McGuinness Associates
  • 98. + Observations from the Wine Agent 98 98  Background knowledge is reasonably simple and built in OWL (includes foods and wine and pairing information similar to the OWL Guide, Ontology Engineering 101, CLASSIC Tutorial, …)  Background knowledge can be used for simple query expansion over wine sources to retrieve for example documents about red wines (including zinfandel, syrah, …)  Background knowledge used to interact with structured queries such as those possible on wine.com  Constraints allows a reasoner like Pellet to infer consequences of the premises and query.  Explanation system (Inference Web) can provide provenance information such as information on the knowledge source (McGuinness’ wine ontology) and data sources (such as wine.com)  Services work could allow automatic “matchmaking” instead of hand coded linkages with web resources Copyright © 2011 Thematix & McGuinness Associates
  • 99. + Trust & Understanding 99 99 If users (humans and agents) are to use, reuse, and integrate system answers, they must trust them. System transparency supports understanding and trust. Even simple “lookup” systems benefit from providing information about their sources. Systems that manipulate information (with sound deduction or potentially unsound heuristics) benefit from providing information about their manipulations. Goal: Provide interoperable infrastructure that supports explanations of sources, assumptions, and answers as an enabler for trust. Copyright © 2011 Thematix & McGuinness Associates
  • 100. + Explanations, Proof Analysis 100 100  Framework for explaining question answering tasks  Stores and manages meta-information about proofs and explanations through a distributed repository  Uses the Proof Markup Language (PML) for proof interchange (OWL-based)  Services include proof presentation, strategy-based views, filtering, trust, combination, expansion, checking, search, and other capabilities  Integrated browsing and display of PML documents from diverse sources  Rewriting capabilities for improved understanding  Multi-modal dialogue options including alternative strategies for presenting explanations, visualizations, and summaries Copyright © 2011 Thematix & McGuinness Associates
  • 101. + Inference Web (IW) 101 101 End Users End-User Data Access & Validate published PML data Interaction Data Analysis services Services Explanation via Graph Distributed Explanation via Summary PML data Explanation via Annotation Access published PML data Inference Web is a semantic web-based knowledge provenance management infrastructure: • PML for encoding and interchange provenance metadata in distributed environment • Interactive explanation services for end-users • Data access and analysis services for enriching the value of knowledge provenance Copyright © 2011 Thematix & McGuinness Associates
  • 102. + Proof Markup Language (PML)  A new kind of linked data on the Web 102 102 World Wide Web D PML PML data Enterprise Web data PML PML PML data D PML data data data D Enterprise Web D D D D PML D data …  Modularized & extensible  Provenance: annotate provenance properties  Justification: encodes provenance relations  Trust: add trust annotation  Semantic Web based Copyright © 2011 Thematix & McGuinness Associates
  • 103. + Technical Architecture IW Registrar call Web Service Edit edit 103 103 PML database Interface Interface machine agents generate domain experts generate edit PML API translate Proofs PML Translator (N3, KIF) PML (provenance, justification, trust) Computation Tools (validation, conflict detection, abstraction) is-stored-as Data Access Presentation Tools (IW Browser) PML Java PML parse PML Web Objects API Documents reads uses harvests & indexes searches harvests IW Search subscribes Copyright © 2011 Thematix & McGuinness Associates
  • 104. Making Systems Actionable using Knowledge Provenance Mobile CALO Wine Agent 104 104 Combining Proofs in TPTP Intelligence Analyst Tools Knowledge Provenance in Virtual GILA NOW including Data-gov Observatories Copyright © 2011 Thematix & McGuinness Associates 104
  • 105. + Example Proof Tree Browser 105 105 Copyright © 2011 Thematix & McGuinness Associates
  • 106. + Explaining Extracted Entities Source: fbi_01.txt (in NL) 106 106 Same conclusion Source Usage: span from 01 to from multiple 78 extractors Conflicting conclusion from one extractor (with annotations) This extractor decided that Person_fbi-01.txt_46 is a Person and not Occupation (in logical format – e.g. KIF) Copyright © 2011 Thematix & McGuinness Associates
  • 107. + Trustworthiness of Ramazi report From FBI 107 107 From intercepts From CIA Copyright © 2011 Thematix & McGuinness Associates