22 owl section 1

1.
OWL Representing Information Using theWeb Ontology Language

2.
1 Current Web •Publishing medium • Dominated by HTML ▫ Hyper Text Markup Language • Pages accessible using URLs ▫ Uniform Resource Locators ▫ http://www.w3.org/ • Supports human readers using browsers

3.
1.1 Current WebHistory • Internet infrastructure created by DARPA • Mostly text-based (telnet, ftp, gopher) • 1992: Tim Berners-Lee/CERT developed ▫ HTML & HTTP (Hyper Text Transfer Protocol) ▫ Web browser (Mosaic) • Allows anyone to publish structured documents connected by hyperlinks • Combined with TCP/IP and XML (eXtensible Markup Language) to create “killer app”

4.
1.3 The Webis Not Enough • Not enough structure to support computer processing of content • No way to connect information to enable complex queries • HTML too focused on format/display • Need to add markup to explain meaning (semantics) • Semantics will enable automated interpretation of structured web content

5.
1.3.1 Information Structure •HTML documents ▫ Semi-structured formatting ▫ Unstructured text • Natural Language Processing (NLP) ▫ Improving, but impractical on a large scale • Structured database information must be shared in a computer-parseable maner • Goal: allow automated software agents to mine the web, creating new functionality

6.
1.3.2 Finding RequiresMetadata  “Find the cheapest Key lime pie within 5 miles.” • Keyword-based search engines ▫ Find pages that might contain desired content ▫ Don’t provide answers to questions…the goal! ▫ Have to find local restaurants, then look at their menus • Query engines aim to answer questions ▫ Should be able to filter restaurants within 5 miles, access menus, compare prices, get answer ▫ Show how answer gotten from reliable sources

7.
1.3.3 Semantics MustBe Explicit • Providing semantic information explicitly in documents enables software to: ▫ Manipulate information (filter, summarize) ▫ Infer new facts (inference) ▫ Link multiple distributed information representations (semantic join)

8.
2.2.2.1 Structured Representations •Computers need ▫ Consistently structured information collections ▫ Inference rules to conduct automated reasoning ▫ Representations formal enough to detect inconsistencies and errors ▫ Network-distributed information to support scalability

9.
2.2.2.2 Supporting Language •Need a tagged markup language to provide ▫ Syntax  Language format rules; open & vendor-neutral ▫ Semantics  Meaning of concepts; formal, finite, & extensible ▫ Expressiveness  Richness; able to express concepts & relationships  Completeness, correctness, & efficiency (hardest!) ▫ Standards  Common language for all

10.
2.2.3 Compromise • Mustbalance need for structure with need for human-friendly data representations ▫ True natural language processing not yet ready ▫ Humans don’t like to process raw structured data • Proposed solution ▫ Humans must augment content with markup ▫ Must show an ROI payoff for extra effort

11.
2.3 Semantic Webto the Rescue • Next evolutionary generation of the web ▫ Structured information representations provide explicit meaning ▫ Information “marked up” according to language standards ▫ Software provides new functionality by interpreting, exchanging, & processing meaning • Technologies focus on information representations tied to explicit meaning

12.
2.3.1 Semantic WebHistory • Term coined by Sir Tim Berners-Lee • US Dept of Defense/DARPA created DAML ▫ DARPA Agent Markup Language ▫ Helped define critical concepts • European Union created OIL ▫ Ontology Interface Layer ▫ Combined with DAML to create DAML+OIL • W3C built on DAML+OIL to create OWL ▫ Web Ontology Language (yes, it’s out of order) ▫ First draft approved February 2004

13.
2.3.2 Semantic WebVision • Next generation of the web • Vast object-oriented integrated knowledge base that can be accessed and inferenced via machine-understandable schemas • Transparent to the end-user • Link documents and the information in them • Leverage the current web infrastructure • Reduce the cost of performing tasks

14.
2.3.4 Use Cases •Tactical level functionality ▫ Lower-level functions & basic operations ▫ Behind the scenes • Strategic applications ▫ Higher-level compositions of tactical features ▫ Provide more complex functionality ▫ Customer-facing

15.
2.3.4.1 Tactical Services •Describe distributed information ▫ Harvest content, process, & exchange results • Support queries ▫ Answer questions & explain reasoning • Support searching ▫ Find information based on meaning, not keywords • Support inferring ▫ Drawing conclusions from explicit facts ▫ Reduces size & complexity of knowledge bases

16.
2.3.4.2 Strategic Applications •Vertical applications ▫ Provide specialized services to a particular domain ▫ E-commerce (B2B, B2C) • Agent software ▫ Autonomous; mobile; architecture-independent ▫ Find & interpret information, act, report results • Information management ▫ Migrate intelligence from the software to the data ▫ Provide new functionality without modifying code ▫ Integrate repositories

17.
2.3.5 Appropriate Applications •Semantic web applications appropriate to: ▫ Publish content for both humans and computers ▫ Share information without understanding model ▫ Inferring new facts & joining information sources • Characteristics of good candidate domains: ▫ Well-understood but dynamic domain ▫ Heterogeneous information sources ▫ Existing information interchange requirements • Not suited to binary data, e.g. image processing

18.
Chapter 3

19.
3.1 Ontology Definitions •Historical definition ▫ Studies of the science of being, and the nature and organization of reality ▫ Definitive classifications of objects & their relationships • Other definitions ▫ Computer science definition ▫ Types of ontologies ▫ Gruber definition ▫ OWL-specific ontology definitions

20.
3.1.1 Computer ScienceDefinition • Popularized by AI community • Tbox ▫ Terminogical components ▫ Equivalent to “schema” ▫ Define concepts ▫ Semantic Web equivalent  Ontology • Abox ▫ Assertional components ▫ Equivalent to “records” ▫ Assert facts ▫ Semantic Web equivalent  Individuals

21.
3.1.2 Types ofOntologies • Many types ▫ Domain ontologies ▫ Metadata ontologies (Dublin Core) ▫ Method/task ontologies • Many ways to classify ontologies ▫ Formality ▫ Regularity ▫ Expressiveness • Simplest ontology: Taxonomy ▫ Hierarchy of concepts related with IS-A relationship ▫ Can’t express complex relationships

22.
3.1.3 Gruber Definition •“Formal specification of a conceptualization” – T. Gruber • An ontology is a ▫ Formally-described ▫ Machine-readable ▫ Collection of terms & their relationships ▫ Expressed in a language ▫ Stored in a file

23.
3.1.4 OWL-Specific OntologyDef’n • Web Ontology Language (OWL) ontology ▫ “An OWL-encoded, web-distributed vocabulary of declarative formalisms describing a model of a domain” • Domain ▫ A specific subject area or area of knowledge ▫ Typically the focus of a particular community of interest • Encode a model of the domain, not all of it

24.
3.2 Ontology Features •Communicate a common understanding of a domain • Example: restaurant association describes relationships between food items • Declare explicit semantics • Make assumptions explicit • Reduce ambiguity • Make expressive statements • Have reasoning properties to support scalable, decidable inferencing • Support sharing of information • Allow semantic mapping between information sources

25.
3.3 Ontology DevelopmentIssues • Authoring ontologies ▫ Can be developed by anyone, but ▫ Better if developed by consensus-based standards development groups ▫ Vertical ontologies describe a domain ▫ Horizontal ontologies span domains and describe basic concepts • Separating ontologies from individuals ▫ Usually a good idea ▫ Sometimes not possible • Committing to an ontology ▫ Makes applications easier to understand, modify, reuse

26.
3.4 Describing Semantics •Defining information representation building blocks • Describing relationships between building blocks • Describing relationships within building blocks

27.
3.4.1 Building Blocks •Three basic blocks ▫ Class constructs ▫ Property constructs ▫ Individual constructs • Together, they describe a model of a domain • Each type requires ▫ A computer-understandable representation ▫ Identifiers for referencing these representations

28.
3.4.1.1 Class Construct •Similar to ▫ “Class” in OO terminology ▫ “Table” in relational DB terminology • Group or set of objects with similar properties or characteristics (explicit or implicit) in common • General statements can be made that apply to all members of the class • Examples ▫ Food ▫ Menu Item ▫ Person

29.
3.4.1.2 Property Construct •Similar to ▫ “Accessor method” in OO terminology ▫ “Columns” or “fields” in relational DB terms • Binary association that relates an object (instance) to a value • Examples ▫ Price ▫ Size • Unlike OO accessors, properties can be associated with multiple unrelated classes!

30.
3.4.1.3 Individuals • Similarto ▫ “Objects” in OO terminology ▫ “Rows” or “records” in relational DB terminology • Represent class object instances in the domain ▫ Physical things ▫ Virtual concepts • Unlike objects, Individuals have no functionality • Examples ▫ KnightOwlRestaurant ▫ Order456 • Difference b/w individuals & classes not always clear • Literal values (“1”, “A”) are special case of individuals

31.
3.4.2 Relating Constructs •Need to describe relationships between building blocks • “is an instance of” ▫ Individual to Class • “has value for” ▫ Individual to Property • Restrictions ▫ Between Class and Property

32.
3.4.2.1 Relate Individuals& Classes • Individuals are members of classes • “Membership” or “is an instance of” relationship • Must be explicitly stated • Examples ▫ “KnightOwlRestaurant” is an instance of “Restaurant” class ▫ “Mark” is an instance of “Person” class

33.
3.4.2.2 Relate Individuals& Properties • Individuals have attributes described by properties • “has value for” relationship • Example ▫ “KeyLimePie” individual has value “$2” for the property “price” ▫ “Mark” individual has value “34” for the property “age”

34.
3.4.2.3 Relate Classes& Properties • Classes can restrict use of Properties in individuals ▫ “IsBrotherOf” property range restricted to “Male”s • Properties can be used to define Classes by defining membership in the class ▫ Individual is member of class “Boy” iff Individual is in “Male” class and “Age” property value <= 18. • Restrictions can constrain Property values ▫ To be of a certain class (range) ▫ To only describe particular classes (domain)

35.
3.4.3 Semantic Relationshipsin Blocks • Must be able to describe semantic relationships within classes, properties, and individuals • Synonymy • Antonymy • Hyponymy • Meronymy

36.
3.4.3.1 Synonymy Relation •Connects concepts with similar meaning ▫ equals() in Java – same meaning, different instance • Stricter form is equivalence (identical) ▫ == in Java – same instance • Class to Class ▫ Noodles & Pasta; Soda & Pop • Instance to Instance ▫ Knight Owl Restaurant & franchiseProperty123 • Property to Property ▫ Cost & Price • Allows merging concepts & linking heterogeneous knowledge bases =

37.
3.4.3.2 Antonymy Relation •Opposite meaning • Stricter form is disjointness • Establishes dichotomy of meaning b/w terms • Class to Class ▫ Regular Price Menu Item & Sale Price Menu Item • Instance to Instance • Property to Property ≠

38.
3.4.3.3 Hyponymy Relation •Specialization & generalization • Creates taxonomic hierarchies • Also called ▫ “is-a” ▫ “inheritance” ▫ “subsumption” • Transitive downward • Better for permanent relationships • Class to Class ▫ Spaghetti “is-a” Pasta ▫ New York Style Pizzeria “is-a” Italian Restaurant “is-a” Restaurant • Property to Property ▫ salePrice “is-a” price Δ

39.
Meronymy/Hyponymy Relation • Aggregation& composition • Also called ▫ “part-of” ▫ “component of” • Mereology (part-whole theory) • Holonymy (whole-part theory) • Closely related to “ownership” • Transitive downward • Class to Class ▫ Meatball “part-of” Spaghetti and Meatballs Dish ▫ Fork “part-of” Place Setting • Individual to individual ▫ Drink Order 321 “part-of” Restaurant Bill 789

40.
3.4.4 Semantics Summary •Building Blocks • Relationships Construct Description A group or set of individual objects with similar characteristics Associates attrib/value pairs with individuals, restricts classes Represents a specific instance object of a class Functionality Relationship Summary Relating blocks to each other Individuals to Classes Membership Individuals to Properties Attribute values Classes to Properties Restrictions Describing relationships Synonymy Similarities Antonymy Differences Hyponymy Specialization Meronymy Part/whole Holonymy Whole/Part

41.
3.5 Ontology Languages •Formal, parseable, & usable by software • Define semantics in context-independent way • Support some level of logic expression • OWL based on: ▫ Frame-based systems ▫ Description logics

42.
3.5.1 Frame-based Systems •Modeling primitives called “frames” (classes) • Properties (attributes) are called “slots” • Property values are called “fillers” • Same slot name usable with different classes ▫ Can specify different range & value restrictions

43.
3.5.2 Description Logics(DLs) • Modeling primitives called “concepts” (classes) • Properties (attributes) are called “roles” • DLs also called “terminological logics” or “concept languages” • Balance expressiveness with “decidability” ▫ Whether software can reach a conclusion or not • DL concepts defined by their objects’ membership constraints ▫ Used to automatically derive classification taxonomies (hierarchies)

44.
3.5.2 Descriptions Logicscont’d • DLs can specify ▫ Class constructors ▫ Property constructors ▫ Axioms relating classes & properties • Allow composite descriptions ▫ E.g. restrictions on relationships between objects • Use first-order logic • Still decidable • Support efficient inferencing

45.
3.6 Ontologies Summary •Various definitions (AI, Gruber, OWL) • Purposes ▫ Communicate specification of domain ▫ Declare explicit semantics ▫ Support information sharing • Different types; taxonomies most common • Divided into Tbox & Abox ▫ Tbox: schema, definitions of concepts ▫ Abox: records, defintions of individuals/objects

46.
3.6 Ontologies Summarycont’d • Building blocks ▫ Class, Property, Individual • Relationships between different block types ▫ Membership, Attribute Values, Restrictions • Relationships between same block types ▫ Synonomy, Antonymy, Hyponymy, Meronymy, Holonymy • Ontologies described using formal languages

47.
Chapter 4

48.
4.1 OWL Features •Primary goals ▫ Intuitive for humans, minimal investment ▫ Expressive, with explicit semantics for software • Can define and/or extend ontologies • Supports scalability (needs some work) • XML-based annotations • Makes statements/assertions about classes, properties, & individuals • Additional facts derived via inferencing

49.
4.2 Layered Architecture Applications} Implementation Layer Ontology Languages (OWL Full, OWL DL, and OWL Lite) } Logical Layer RDF Schema Individuals } Ontological Primitive Layer RDF and RDF/XML } Basic Relational Language Layer XML and XMLS Datatypes } Transport/Syntax Layer URIs and Namespaces } Symbol/Reference Layer

50.
4.4 OWL IntroductionSummary • Web Ontology Language (OWL) ▫ Defined by the W3C ▫ Used to make statements about  Classes  Properties  Individuals ▫ Designed as a layered architecture built on  URIs & Namespaces  XML & XMLS  RDF & RDFS

51.
Backup – Entireslide set

52.
OWL Representing Information Using theWeb Ontology Language

53.
Section 1

54.
Section 1 • Chapter1: Historical Web ▫ Web history, context, features, & shortcomings • Chapter 2: Semantic Web ▫ Challenges, requirements, & solutions • Chapter 3: Ontologies ▫ Concepts, purposes, relationships, features, & languages • Chapter 4: OWL Introduction ▫ OWL language, layered architecture, & supporting technologies

55.
Chapter 1

56.
1 Current Web •Publishing medium • Dominated by HTML ▫ Hyper Text Markup Language • Pages accessible using URLs ▫ Uniform Resource Locators ▫ http://www.w3.org/ • Supports human readers using browsers

57.
1.1 Current WebHistory • Internet infrastructure created by DARPA • Mostly text-based (telnet, ftp, gopher) • 1992: Tim Berners-Lee/CERT developed ▫ HTML & HTTP (Hyper Text Transfer Protocol) ▫ Web browser (Mosaic) • Allows anyone to publish structured documents connected by hyperlinks • Combined with TCP/IP and XML (eXtensible Markup Language) to create “killer app”

58.
1.2 Current WebCharacteristics • Features • Benefits • Applications

59.
1.2.1 Current WebFeatures • Diverse • Document-centric • Virtual repository of information • No controlling authority • Managed by open standards from W3C ▫ World Wide Web Consortium • Intended for human access & reading

60.
1.2.2 Current WebBenefits • Superior to private networks • Transactions are cheaper (self-service) • Cheap to communicate world-wide • Created online communities ▫ Open-source movement – free high-quality tools ▫ Countless online forums

61.
1.2.3 Current WebApplications • Most content designed for humans • Variety of purposes ▫ E-commerce ▫ Education ▫ Financial services ▫ Auctions ▫ Music • Many sites use generated HTML & XML generated from databases

62.
1.3 The Webis Not Enough • Not enough structure to support computer processing of content • No way to connect information to enable complex queries • HTML too focused on format/display • Need to add markup to explain meaning (semantics) • Semantics will enable automated interpretation of structured web content

63.
1.3.1 Information Structure •HTML documents ▫ Semi-structured formatting ▫ Unstructured text • Natural Language Processing (NLP) ▫ Improving, but impractical on a large scale • Structured database information must be shared in a computer-parseable maner • Goal: allow automated software agents to mine the web, creating new functionality

64.
1.3.2 Finding RequiresMetadata  “Find the cheapest Key lime pie within 5 miles.” • Keyword-based search engines ▫ Find pages that might contain desired content ▫ Don’t provide answers to questions…the goal! ▫ Have to find local restaurants, then look at their menus • Query engines aim to answer questions ▫ Should be able to filter restaurants within 5 miles, access menus, compare prices, get answer ▫ Show how answer gotten from reliable sources

65.
1.3.3 Semantics MustBe Explicit • Providing semantic information explicitly in documents enables software to: ▫ Manipulate information (filter, summarize) ▫ Infer new facts (inference) ▫ Link multiple distributed information representations (semantic join)

66.
1.4 Current WebSummary • Current Web ▫ Document-centric ▫ Focused on humans using browsers ▫ Insufficient for automated data processing • New technologies needed ▫ Structure information for automated processing ▫ Improve searches ▫ Link disparate data sources with each other • The Semantic Web!

67.
Chapter 2

68.
2 Semantic WebIntroduction • Web information representation challenges • Requirements for a solution • Semantic Web concepts that satisfy those requirements

69.
2.1 Web InformationRepresentation Challenges • Increased Need for Information Representation • Ambiguous Human Descriptions • Software Demands for Specificity

70.
2.1.1 Information Representation •Volume of information increasing exponentially • User expectations of the Internet also growing • To satisfy expectations, we need more than just HTML, XML & databases

71.
2.1.2 Ambiguous Descriptions •Many human information formats ▫ Specialized domains with unique terminology ▫ Regional language differences ▫ Many sublanguages within communities ▫ Difficult to get consensus • Language agreement impossible • Meta-language agreement possible ▫ Language to express language • We need a language that can represent information from many domains

72.
2.1.3 Demands forSpecificity • Computers need information to be ▫ Structured ▫ Consistent ▫ Well-formed ▫ Logical

73.
2.2 Requirements fora Solution • Minimize Human Investment • Satisfy Computer Requirements • Compromise between these goals

74.
2.2.1 Minimize HumanInvestment • Information Representation Producers • Information Representation Consumers • Requirements common to both

75.
2.2.1.1 Representation Producers •Provide content from existing sources • Aim to generate information representations ▫ Quickly ▫ Effectively ▫ Inexpensively • Represent data using natural models that are ▫ Extendable ▫ Versionable ▫ Configuration-managed

76.
2.2.1.2 Representation Consumers •Aim to create software to ▫ Parse information ▫ Interpret information ▫ Manipulate information • Software should be able to ▫ Combine information from different domains ▫ Use others’ data without needing to understand the underlying data model ▫ Reduce human intervention

77.
2.2.1.3 Requirements Commonto Both • Solution must be ▫ Inexpensive ▫ Easy to implement ▫ Intuitive ▫ Evolutionary, not revolutionary ▫ Compatible with existing web standards

78.
2.2.2 Satisfy ComputerRequirements • Structured distributed representations to enable applications • Supporting language

79.
2.2.2.1 Structured Representations •Computers need ▫ Consistently structured information collections ▫ Inference rules to conduct automated reasoning ▫ Representations formal enough to detect inconsistencies and errors ▫ Network-distributed information to support scalability

80.
2.2.2.2 Supporting Language •Need a tagged markup language to provide ▫ Syntax  Language format rules; open & vendor-neutral ▫ Semantics  Meaning of concepts; formal, finite, & extensible ▫ Expressiveness  Richness; able to express concepts & relationships  Completeness, correctness, & efficiency (hardest!) ▫ Standards  Common language for all

81.
2.2.3 Compromise • Mustbalance need for structure with need for human-friendly data representations ▫ True natural language processing not yet ready ▫ Humans don’t like to process raw structured data • Proposed solution ▫ Humans must augment content with markup ▫ Must show an ROI payoff for extra effort

82.
2.3 Semantic Webto the Rescue • Next evolutionary generation of the web ▫ Structured information representations provide explicit meaning ▫ Information “marked up” according to language standards ▫ Software provides new functionality by interpreting, exchanging, & processing meaning • Technologies focus on information representations tied to explicit meaning

83.
2.3.1 Semantic WebHistory • Term coined by Sir Tim Berners-Lee • US Dept of Defense/DARPA created DAML ▫ DARPA Agent Markup Language ▫ Helped define critical concepts • European Union created OIL ▫ Ontology Interface Layer ▫ Combined with DAML to create DAML+OIL • W3C built on DAML+OIL to create OWL ▫ Web Ontology Language (yes, it’s out of order) ▫ First draft approved February 2004

84.
2.3.2 Semantic WebVision • Next generation of the web • Vast object-oriented integrated knowledge base that can be accessed and inferenced via machine-understandable schemas • Transparent to the end-user • Link documents and the information in them • Leverage the current web infrastructure • Reduce the cost of performing tasks

85.
2.3.3 Populating theSemantic Web • Developing representation standards ▫ Scope the domain/analyze requirements ▫ Define terms and relationships ▫ Encode vocabulary & relationships (ontology) ▫ Publish representation on servers • Requires significant up-front effort, but • Yields greater returns than current solutions • Cost reduces as reuse grows

86.
2.3.4 Use Cases •Tactical level functionality ▫ Lower-level functions & basic operations ▫ Behind the scenes • Strategic applications ▫ Higher-level compositions of tactical features ▫ Provide more complex functionality ▫ Customer-facing

87.
2.3.4.1 Tactical Services •Describe distributed information ▫ Harvest content, process, & exchange results • Support queries ▫ Answer questions & explain reasoning • Support searching ▫ Find information based on meaning, not keywords • Support inferring ▫ Drawing conclusions from explicit facts ▫ Reduces size & complexity of knowledge bases

88.
2.3.4.2 Strategic Applications •Vertical applications ▫ Provide specialized services to a particular domain ▫ E-commerce (B2B, B2C) • Agent software ▫ Autonomous; mobile; architecture-independent ▫ Find & interpret information, act, report results • Information management ▫ Migrate intelligence from the software to the data ▫ Provide new functionality without modifying code ▫ Integrate repositories

89.
2.3.5 Appropriate Applications •Semantic web applications appropriate to: ▫ Publish content for both humans and computers ▫ Share information without understanding model ▫ Inferring new facts & joining information sources • Characteristics of good candidate domains: ▫ Well-understood but dynamic domain ▫ Heterogeneous information sources ▫ Existing information interchange requirements • Not suited to binary data, e.g. image processing

90.
2.4 Semantic WebIntro Summary • Existing challenges ▫ Humans want information in readable formats ▫ Computers need structured formats ▫ Solution must minimize human investment, but meet computer needs • Semantic web is the solution ▫ Builds on the existing web ▫ Supplies new information representation features ▫ Presents information understandable to both

91.
Chapter 3

92.
3 Ontologies Enablethe Semantic Web • Ontology definitions • Development issues • Description methods • Ontology features • Language issues

93.
3.1 Ontology Definitions •Historical definition ▫ Studies of the science of being, and the nature and organization of reality ▫ Definitive classifications of objects & their relationships • Other definitions ▫ Computer science definition ▫ Types of ontologies ▫ Gruber definition ▫ OWL-specific ontology definitions

94.
3.1.1 Computer ScienceDefinition • Popularized by AI community • Tbox ▫ Terminogical components ▫ Equivalent to “schema” ▫ Define concepts ▫ Semantic Web equivalent  Ontology • Abox ▫ Assertional components ▫ Equivalent to “records” ▫ Assert facts ▫ Semantic Web equivalent  Individuals

95.
3.1.2 Types ofOntologies • Many types ▫ Domain ontologies ▫ Metadata ontologies (Dublin Core) ▫ Method/task ontologies • Many ways to classify ontologies ▫ Formality ▫ Regularity ▫ Expressiveness • Simplest ontology: Taxonomy ▫ Hierarchy of concepts related with IS-A relationship ▫ Can’t express complex relationships

96.
3.1.3 Gruber Definition •“Formal specification of a conceptualization” – T. Gruber • An ontology is a ▫ Formally-described ▫ Machine-readable ▫ Collection of terms & their relationships ▫ Expressed in a language ▫ Stored in a file

97.
3.1.4 OWL-Specific OntologyDef’n • Web Ontology Language (OWL) ontology ▫ “An OWL-encoded, web-distributed vocabulary of declarative formalisms describing a model of a domain” • Domain ▫ A specific subject area or area of knowledge ▫ Typically the focus of a particular community of interest • Encode a model of the domain, not all of it

98.
3.2 Ontology Features •Communicate a common understanding of a domain • Declare explicit semantics • Make expressive statements • Support sharing of information

99.
3.2.1 Domain Understanding •Provided by communities of interest ▫ Example: restaurant association describes relationships between food items • Ontology formally documents one common understanding of a domain ▫ Reduces misunderstanding • Shared and common understanding communicated between humans and software systems

100.
3.2.2 Explicit Semantics •Semantics ▫ Formal descriptions of terms and relationships ▫ Traditionally coded into the software or schema ▫ Document concepts using modeling primitives and semantic relationships ▫ Make assumptions explicit ▫ Reduce ambiguity ▫ Enable interoperability • Must be described formally to be processed

101.
3.2.3 Expressiveness • “Extensiveness”of the ontology • Must be expressive enough to ▫ Represent formal semantics ▫ Have reasoning properties to support inferencing • Support canonical granular representations • Limited to keep reasoning ▫ Decidable ▫ Scaleable

102.
3.2.4 Sharing Information •OWL-compliant software can ▫ Manipulate information internally ▫ Interoperate with other software ▫ Do semantic mapping between information sources • Need to have a shared language and access to information

103.
3.3 Ontology DevelopmentIssues • Authoring ontologies ▫ Can be developed by anyone, but ▫ Better if developed by consensus-based standards development groups ▫ Vertical ontologies describe a domain ▫ Horizontal ontologies span domains and describe basic concepts • Separating ontologies from individuals ▫ Usually a good idea ▫ Sometimes not possible • Committing to an ontology ▫ Makes applications easier to understand, modify, reuse

104.
3.4 Describing Semantics •Defining information representation building blocks • Describing relationships between building blocks • Describing relationships within building blocks

105.
3.4.1 Building Blocks •Three basic blocks ▫ Class constructs ▫ Property constructs ▫ Individual constructs • Together, they describe a model of a domain • Each type requires ▫ A computer-understandable representation ▫ Identifiers for referencing these representations

106.
3.4.1.1 Class Construct •Similar to ▫ “Class” in OO terminology ▫ “Table” in relational DB terminology • Group or set of objects with similar properties or characteristics (explicit or implicit) in common • General statements can be made that apply to all members of the class • Examples ▫ Food ▫ Menu Item ▫ Person

107.
3.4.1.2 Property Construct •Similar to ▫ “Accessor method” in OO terminology ▫ “Columns” or “fields” in relational DB terms • Binary association that relates an object (instance) to a value • Examples ▫ Price ▫ Size • Unlike OO accessors, properties can be associated with multiple unrelated classes!

108.
3.4.1.3 Individuals • Similarto ▫ “Objects” in OO terminology ▫ “Rows” or “records” in relational DB terminology • Represent class object instances in the domain ▫ Physical things ▫ Virtual concepts • Unlike objects, Individuals have no functionality • Examples ▫ KnightOwlRestaurant ▫ Order456 • Difference b/w individuals & classes not always clear • Literal values (“1”, “A”) are special case of individuals

109.
3.4.2 Relating Constructs •Need to describe relationships between building blocks • “is an instance of” ▫ Individual to Class • “has value for” ▫ Individual to Property • Restrictions ▫ Between Class and Property

110.
3.4.2.1 Relate Individuals& Classes • Individuals are members of classes • “Membership” or “is an instance of” relationship • Must be explicitly stated • Examples ▫ “KnightOwlRestaurant” is an instance of “Restaurant” class ▫ “Mark” is an instance of “Person” class

111.
3.4.2.2 Relate Individuals& Properties • Individuals have attributes described by properties • “has value for” relationship • Example ▫ “KeyLimePie” individual has value “$2” for the property “price” ▫ “Mark” individual has value “34” for the property “age”

112.
3.4.2.3 Relate Classes& Properties • Classes can restrict use of Properties in individuals ▫ “IsBrotherOf” property range restricted to “Male”s • Properties can be used to define Classes by defining membership in the class ▫ Individual is member of class “Boy” iff Individual is in “Male” class and “Age” property value <= 18. • Restrictions can constrain Property values ▫ To be of a certain class (range) ▫ To only describe particular classes (domain)

113.
3.4.3 Semantic Relationshipsin Blocks • Must be able to describe semantic relationships within classes, properties, and individuals • Synonymy • Antonymy • Hyponymy • Meronymy

114.
3.4.3.1 Synonymy Relation •Connects concepts with similar meaning ▫ equals() in Java – same meaning, different instance • Stricter form is equivalence (identical) ▫ == in Java – same instance • Class to Class ▫ Noodles & Pasta; Soda & Pop • Instance to Instance ▫ Knight Owl Restaurant & franchiseProperty123 • Property to Property ▫ Cost & Price • Allows merging concepts & linking heterogeneous knowledge bases =

115.
3.4.3.2 Antonymy Relation •Opposite meaning • Stricter form is disjointness • Establishes dichotomy of meaning b/w terms • Class to Class ▫ Regular Price Menu Item & Sale Price Menu Item • Instance to Instance • Property to Property ≠

116.
3.4.3.3 Hyponymy Relation •Specialization & generalization • Creates taxonomic hierarchies • Also called ▫ “is-a” ▫ “inheritance” ▫ “subsumption” • Transitive downward • Better for permanent relationships • Class to Class ▫ Spaghetti “is-a” Pasta ▫ New York Style Pizzeria “is-a” Italian Restaurant “is-a” Restaurant • Property to Property ▫ salePrice “is-a” price Δ

117.
Meronymy/Hyponymy Relation • Aggregation& composition • Also called ▫ “part-of” ▫ “component of” • Mereology (part-whole theory) • Holonymy (whole-part theory) • Closely related to “ownership” • Transitive downward • Class to Class ▫ Meatball “part-of” Spaghetti and Meatballs Dish ▫ Fork “part-of” Place Setting • Individual to individual ▫ Drink Order 321 “part-of” Restaurant Bill 789

118.
3.4.4 Semantics Summary •Building Blocks • Relationships Construct Description A group or set of individual objects with similar characteristics Associates attrib/value pairs with individuals, restricts classes Represents a specific instance object of a class Functionality Relationship Summary Relating blocks to each other Individuals to Classes Membership Individuals to Properties Attribute values Classes to Properties Restrictions Describing relationships Synonymy Similarities Antonymy Differences Hyponymy Specialization Meronymy Part/whole Holonymy Whole/Part

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3.5 Ontology Languages •Formal, parseable, & usable by software • Define semantics in context-independent way • Support some level of logic expression • OWL based on: ▫ Frame-based systems ▫ Description logics

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3.5.1 Frame-based Systems •Modeling primitives called “frames” (classes) • Properties (attributes) are called “slots” • Property values are called “fillers” • Same slot name usable with different classes ▫ Can specify different range & value restrictions

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3.5.2 Description Logics(DLs) • Modeling primitives called “concepts” (classes) • Properties (attributes) are called “roles” • DLs also called “terminological logics” or “concept languages” • Balance expressiveness with “decidability” ▫ Whether software can reach a conclusion or not • DL concepts defined by their objects’ membership constraints ▫ Used to automatically derive classification taxonomies (hierarchies)

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3.5.2 Descriptions Logicscont’d • DLs can specify ▫ Class constructors ▫ Property constructors ▫ Axioms relating classes & properties • Allow composite descriptions ▫ E.g. restrictions on relationships between objects • Use first-order logic • Still decidable • Support efficient inferencing

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3.6 Ontologies Summary •Various definitions (AI, Gruber, OWL) • Purposes ▫ Communicate specification of domain ▫ Declare explicit semantics ▫ Support information sharing • Different types; taxonomies most common • Divided into Tbox & Abox ▫ Tbox: schema, definitions of concepts ▫ Abox: records, defintions of individuals/objects

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3.6 Ontologies Summarycont’d • Building blocks ▫ Class, Property, Individual • Relationships between different block types ▫ Membership, Attribute Values, Restrictions • Relationships between same block types ▫ Synonomy, Antonymy, Hyponymy, Meronymy, Holonymy • Ontologies described using formal languages

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Chapter 4

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4 OWL Introduction •OWL Features • Semantic Web’s Layered Architecture

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4.1 OWL Features •Primary goals ▫ Intuitive for humans, minimal investment ▫ Expressive, with explicit semantics for software • Can define and/or extend ontologies • Supports scalability (needs some work) • XML-based annotations • Makes statements/assertions about classes, properties, & individuals • Additional facts derived via inferencing

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4.2 Layered Architecture Applications} Implementation Layer Ontology Languages (OWL Full, OWL DL, and OWL Lite) } Logical Layer RDF Schema Individuals } Ontological Primitive Layer RDF and RDF/XML } Basic Relational Language Layer XML and XMLS Datatypes } Transport/Syntax Layer URIs and Namespaces } Symbol/Reference Layer

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4.2 Layered Architecturecont’d • Layers illustrate rough dependencies ▫ Each layer uses features of lower layers • Implementation Layer ▫ Provides specific applications • Logical Layer ▫ OWL supports formal semantics and reasoning • Ontological Primitive Layer ▫ RDFS defines vocabulary ▫ Individuals defined in RDF RDF Schema Individuals XML and XMLS Datatypes URIs and Namespaces Applications Ontology Languages (OWL Full, OWL DL, and OWL Lite) RDF and RDF/XML

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4.2 Layered Architecturecont’d • Relational Language Layer ▫ RDF’s simple data model & syntax for making statements ▫ Serialized as  RDF/XML or  N-triples • Transport/Syntax Layer ▫ Define primitive datatypes ▫ Provide encoding format • Symbolic/Reference Layer ▫ Identify and reference classes, properties, and individuals RDF Schema Individuals XML and XMLS Datatypes URIs and Namespaces Applications Ontology Languages (OWL Full, OWL DL, and OWL Lite) RDF and RDF/XML

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4.3 Technology Supportfor Layers • Symbol/Reference Layer ▫ Provides identifiers & references to objects described in ontologies and instance files • Transport/Syntax Layer ▫ XML used to serialize OWL syntax ▫ XMLS defines standard datatypes • Basic Relational Layer ▫ RDF makes statements using Attribute/Value pairs to describe objects

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4.3 Tech Supportfor Layers, cont’d • Ontological Primitive Layer ▫ RDFS provides basic vocabulary describing  Classes and subclasses  Properties and subproperties ▫ Instances & property values specified by RDF & XMLS • Logical Layer ▫ OWL dialects (Full, DL, Lite) enhance RDFS • Implementation Layer ▫ Applications built using OWL • Additional layers being considered for rules & trust

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4.4 OWL IntroductionSummary • Web Ontology Language (OWL) ▫ Defined by the W3C ▫ Used to make statements about  Classes  Properties  Individuals ▫ Designed as a layered architecture built on  URIs & Namespaces  XML & XMLS  RDF & RDFS

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