This document discusses a lecture on designing ontologies and using them to build semantic applications. The lecture will cover foundations of semantic web technologies, methodologies for semantic content management, designing semantic content management systems, and designing interactive ubiquitous information systems. It provides an outline that includes discussions on ontologies, ontology design, transformation and refactoring, and using ontology networks in content management systems platforms.
This document discusses semantifying content management systems (CMS) by extracting semantics from CMS and representing them as ontologies. It introduces CMS standards like JCR and CMIS that define repository models. It then presents a generic repository model to represent CMS objects from different specifications. The document discusses using bridges to extract semantics from CMS as ontology classes, properties and individuals. It emphasizes the need for a backend knowledge base to store and reason over the extracted ontological representation. Finally, it discusses enhancing content discovery in CMS by querying and aligning with the extracted semantics and external domain ontologies.
This document provides an overview of a lecture on designing interactive, knowledge-supported, ubiquitous information systems. It discusses foundations of semantic web technologies and content management systems. It then covers principles for designing information systems, including linking systems to the real world and integrating stakeholders. Finally, it proposes a situational design method called SiDIS that uses conceptual models to design flexible systems that can handle unpredictable events. The method focuses on social interactions supported by technical services.
The document outlines the requirements engineering process for developing semantic enhancements to content management systems (CMS). It begins with analyzing existing CMS technologies and gathering requirements from CMS vendors. Ten high-level requirements are identified, including the need for a common vocabulary, semantic tagging and searching, reasoning over content, and multilingual support. Each high-level requirement is then refined into use cases and resulting functional, data, and non-functional requirements.
This document provides an overview of a lecture on designing semantic content management systems (CMS). It introduces the reference architecture for semantic CMS, which includes layers for semantic user interaction, knowledge access, knowledge extraction pipelines, reasoning, knowledge models, and a knowledge repository. It then describes each component of the reference architecture and provides an example implementation of the architecture in the IKS project, which uses semantic technologies to extend an existing CMS with semantic capabilities. Evaluation results are also presented for various semantic features implemented in the project, including entity recognition, classification, and clustering.
The document discusses designing a semantic content management system (CMS) using a RESTful architecture. It covers representing resources with URIs, using content negotiation and HTTP methods to manipulate resources, and returning resources in different formats like JSON, XML, and HTML. The architecture aims to be scalable by using stateless components and caching.
The document outlines the organization and content of a course on semantic content management systems (CMS). It discusses the lecturer's background and contact information. It then details the schedule for lectures, exercises, and exams. The course will take a combined approach of lectures, exercises, and homework to explore content management, the semantic web, semantic CMS, and methodologies for developing semantic CMS. The course content is organized into 10 parts that cover foundations of content management and semantic web technologies, semantic content management, knowledge representation and reasoning, semantic lifting, storing and accessing semantic data, and designing interactive ubiquitous information systems.
The document provides an overview of training material created by the IKS project for university teaching. The material includes lectures on topics related to semantics, content and knowledge management, and semantic content management systems. It introduces the concepts, presents the vision of the semantic web, and describes underlying technologies. The lectures are designed to be used either as a consistent curriculum or integrated into existing courses on selected topics.
The document discusses semantic lifting for content management systems. Semantic lifting refers to associating content items with semantic metadata to make implicit metadata explicit. It involves semantic reengineering of structured data and semantic enhancement of unstructured content through information extraction and classification. Requirements for semantic lifting include generating semantic associations, harmonizing metadata, enabling semantic linking of content, and allowing customization.
This document discusses semantifying content management systems (CMS) by extracting semantics from CMS and representing them as ontologies. It introduces CMS standards like JCR and CMIS that define repository models. It then presents a generic repository model to represent CMS objects from different specifications. The document discusses using bridges to extract semantics from CMS as ontology classes, properties and individuals. It emphasizes the need for a backend knowledge base to store and reason over the extracted ontological representation. Finally, it discusses enhancing content discovery in CMS by querying and aligning with the extracted semantics and external domain ontologies.
This document provides an overview of a lecture on designing interactive, knowledge-supported, ubiquitous information systems. It discusses foundations of semantic web technologies and content management systems. It then covers principles for designing information systems, including linking systems to the real world and integrating stakeholders. Finally, it proposes a situational design method called SiDIS that uses conceptual models to design flexible systems that can handle unpredictable events. The method focuses on social interactions supported by technical services.
The document outlines the requirements engineering process for developing semantic enhancements to content management systems (CMS). It begins with analyzing existing CMS technologies and gathering requirements from CMS vendors. Ten high-level requirements are identified, including the need for a common vocabulary, semantic tagging and searching, reasoning over content, and multilingual support. Each high-level requirement is then refined into use cases and resulting functional, data, and non-functional requirements.
This document provides an overview of a lecture on designing semantic content management systems (CMS). It introduces the reference architecture for semantic CMS, which includes layers for semantic user interaction, knowledge access, knowledge extraction pipelines, reasoning, knowledge models, and a knowledge repository. It then describes each component of the reference architecture and provides an example implementation of the architecture in the IKS project, which uses semantic technologies to extend an existing CMS with semantic capabilities. Evaluation results are also presented for various semantic features implemented in the project, including entity recognition, classification, and clustering.
The document discusses designing a semantic content management system (CMS) using a RESTful architecture. It covers representing resources with URIs, using content negotiation and HTTP methods to manipulate resources, and returning resources in different formats like JSON, XML, and HTML. The architecture aims to be scalable by using stateless components and caching.
The document outlines the organization and content of a course on semantic content management systems (CMS). It discusses the lecturer's background and contact information. It then details the schedule for lectures, exercises, and exams. The course will take a combined approach of lectures, exercises, and homework to explore content management, the semantic web, semantic CMS, and methodologies for developing semantic CMS. The course content is organized into 10 parts that cover foundations of content management and semantic web technologies, semantic content management, knowledge representation and reasoning, semantic lifting, storing and accessing semantic data, and designing interactive ubiquitous information systems.
The document provides an overview of training material created by the IKS project for university teaching. The material includes lectures on topics related to semantics, content and knowledge management, and semantic content management systems. It introduces the concepts, presents the vision of the semantic web, and describes underlying technologies. The lectures are designed to be used either as a consistent curriculum or integrated into existing courses on selected topics.
The document discusses semantic lifting for content management systems. Semantic lifting refers to associating content items with semantic metadata to make implicit metadata explicit. It involves semantic reengineering of structured data and semantic enhancement of unstructured content through information extraction and classification. Requirements for semantic lifting include generating semantic associations, harmonizing metadata, enabling semantic linking of content, and allowing customization.
This document discusses semantic data and technologies for storing and querying semantic data. It provides an overview of semantic data and the semantic web. It then discusses specific technologies for storing semantic data such as triple stores and databases. It also covers query languages for semantic data like SPARQL and provides examples of semantic data storage systems and architectures.
(1) The document discusses the Semantic Web and its underlying technologies. It introduces concepts like RDF, which provides a standard model for data interchange on the Web.
(2) RDF uses URIs to identify resources and describes relationships between resources using predicate-object pairs. This allows expressing the semantics of information in a machine-readable way.
(3) The document explains RDF syntax like statements and serialization formats including RDF/XML. It also covers RDF concepts such as unique resource identification, data structuring using XML, and description of resources, properties and values.
This document discusses interaction patterns for semantic content management systems. It defines interaction patterns as describing recurring user actions when interacting with a computer to achieve tasks. An interaction pattern consists of the problem, the pattern/solution, use cases, and how the pattern applies. The document provides examples of interaction patterns and discusses standards for semantic annotation, presentation, and interaction to enable semantic interaction.
The document discusses content management systems and semantic content management. It introduces content management systems as tools for handling large amounts of content through creation, editing, organization, and presentation. It notes shortcomings of traditional CMS in allowing machines to understand content and infer knowledge. The document proposes that semantic web technologies can help overcome these shortcomings by giving meaning to information to allow both automated and human understanding and interaction with content. It discusses moving from data to information, knowledge, and wisdom through semantic enrichment of content.
The IKS Project aims to build an open source technology platform for semantically enhanced content management systems (CMS). The project is co-funded by the European Union and brings together seven research partners and six industrial partners. The mission is to close gaps in engineering, research, and industry adoption by developing semantic capabilities for CMS frameworks and enabling direct user interaction with knowledge objects.
Multilingual Knowledge Organization Systems Management: Best PracticesMauro Dragoni
This presentation addresses the most well-known challenges in managing multilingual knowledge organization systems.
Such challenges are presented and it is discussed how they have been addressed with the implementation of a collaborative tool called MoKi.
Semantic Wiki: Social Semantic Web In Action: Jesse Wang
This document discusses semantic wikis and Project Halo. It provides an overview of semantic wikis, what they are, how they work, and examples of semantic wiki software. It then discusses Project Halo, its goals of addressing problems with knowledge bases, focus areas including AURA and SILK, and using wikis and crowdsourcing for knowledge acquisition.
Geo-annotations in Semantic Digital Libraries mdabrowski
The document discusses using geo-annotations and ontologies in digital libraries. It describes JeromeDL, a social semantic digital library that allows users to collaboratively annotate resources with metadata like geotags. It also describes the MarcOnt initiative which aims to develop tools for a collaborative ontology about bibliographic resources to improve interoperability between digital libraries and enable semantic search.
The document proposes a collaborative ontology building project (COB) that uses a multi-agent approach to facilitate distributed ontology editing and discovery. Key challenges addressed include making ontology editing easy for non-experts, enabling iterative ontology evolution through expert and agent cooperation, and facilitating ontology mining from distributed and dynamic data sources on the web. The proposed system design involves an ontology repository, various human and software agents that contribute to and validate ontologies, and techniques for tasks like ontology alignment and redundancy/conflict checking.
The document discusses the evolution of the internet and web technologies. It describes early technologies like Vannevar Bush's memex and hypertext, the development of the World Wide Web through HTTP and HTML. It outlines the rise of user-generated content through blogs, photos, video and social sharing sites. It also discusses the potential for machines to understand semantic meaning through standards like XML, RDF and ontologies.
1) The document discusses Simon Buckingham Shum's work developing tools to make conversations and collective sensemaking more visible, including Compendium software for capturing and linking ideas.
2) It describes how issue mapping and design rationale tools have evolved over time from early prototypes like gIBIS to more sophisticated modern platforms.
3) Finally, it outlines Buckingham Shum's research applying tools like Compendium to help analyze and summarize complex topics, scaffold creative skills, and plan for emergency response and personnel recovery situations through conversational modeling.
This document discusses the state-of-the-art of Internet of Things (IoT) ontologies. It begins by defining ontology and describing important design criteria for ontologies including clarity, coherence, extendibility, and minimal encoding bias. It then discusses the challenges of IoT, including large scale networks, deep heterogeneity, and unknown topology. Several existing IoT ontologies are described, including SWAMO, MMI Device Ontology, and SSN. The document concludes that while no single global IoT ontology currently exists, ontologies are needed to address the semantic interoperability challenges of heterogeneous IoT devices and domains.
The document discusses principles for designing reusable learning objects and human-computer interaction. It describes learning objects as small instructional components that can be reused, describing programming languages like Scratch and Squeak that allow creating them. It also discusses universal design principles for education, ensuring representation, expression and engagement for all learners.
This research paper provides an overview of software engineering and object-oriented design and programming. It discusses the history and evolution of software engineering, from early frameworks and methodologies to more modern agile and experimental approaches. Object-oriented concepts like classes, objects, inheritance and polymorphism are explained. Key object-oriented programming languages like Java, C++ and Python are also covered. The paper then focuses on constructs in Java, including classes, objects, methods, data types and control flow statements.
Jist tutorial semantic wikis and applicationsJesse Wang
This document provides an overview of a tutorial on semantic wikis and applications. It introduces the instructors Jesse Wang and Mark Greaves from Vulcan Inc., and Justin Zhang and Ning Hu from TeamMersion LLC. The tutorial covers topics like Semantic MediaWiki (SMW), SMW+, hands-on sessions, and connecting SMW to other systems. It aims to address challenges in building large knowledge bases by acquiring knowledge at scale and lower costs.
The document discusses semantic interoperability within a company. It describes several tools that can be used to describe and structure semantics, including ontologies, tagging, classifications, and taxonomies. It provides examples of how these tools can be applied at an enterprise level, including enterprise ontologies, tag clouds, the Zachman framework, and IBM's Information Framework.
The research group Agile Knowledge Engineering & Semantic Web (AKSW) was founded in 2006 and is now part of the Institute for Applied Informatics at the University of Leipzig. The AKSW aims to advance semantic web, knowledge engineering, and software engineering science and also bridges the gap between research results and applications. The AKSW team actively works on several funded projects involving knowledge management, semantic collaboration platforms, and applying semantic web technologies to applications like tourism information and requirements engineering.
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This presentation addresses the most well-known challenges in managing multilingual knowledge organization systems.
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Semantic Wiki: Social Semantic Web In Action: Jesse Wang
This document discusses semantic wikis and Project Halo. It provides an overview of semantic wikis, what they are, how they work, and examples of semantic wiki software. It then discusses Project Halo, its goals of addressing problems with knowledge bases, focus areas including AURA and SILK, and using wikis and crowdsourcing for knowledge acquisition.
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The document proposes a collaborative ontology building project (COB) that uses a multi-agent approach to facilitate distributed ontology editing and discovery. Key challenges addressed include making ontology editing easy for non-experts, enabling iterative ontology evolution through expert and agent cooperation, and facilitating ontology mining from distributed and dynamic data sources on the web. The proposed system design involves an ontology repository, various human and software agents that contribute to and validate ontologies, and techniques for tasks like ontology alignment and redundancy/conflict checking.
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This document provides an overview of a tutorial on semantic wikis and applications. It introduces the instructors Jesse Wang and Mark Greaves from Vulcan Inc., and Justin Zhang and Ning Hu from TeamMersion LLC. The tutorial covers topics like Semantic MediaWiki (SMW), SMW+, hands-on sessions, and connecting SMW to other systems. It aims to address challenges in building large knowledge bases by acquiring knowledge at scale and lower costs.
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Similar to Lecture knowledge representationreasoning (20)
1. Design of
Ontologies and
Semantic CMS Community their usage for
building Semantic
Applications
Lecturer
Organization
Date of presentation
Co-funded by the
European Union
2. Page: 2
Part I: Foundations
(1) Introduction of Content (2) Foundations of Semantic
Management Web Technologies
Part II: Semantic Content Part III: Methodologies
Management
Knowledge Interaction Requirements Engineering
(3) (7)
and Presentation for Semantic CMS
Knowledge Representation Designing
(4) and Reasoning
(8) Semantic CMS
Semantifying
(5) Semantic Lifting (9)
your CMS
Storing and Accessing Designing Interactive
(6) Semantic Data
(10) Ubiquitous IS
www.iks-project.eu Copyright IKS Consortium
3. Page: 3
What is this Lecture about?
We have learned how ... Part II: Semantic Content
... knowledge can be presented Management
to the user. Knowledge Interaction
(3)
... to provide the user the ability and Presentation
to interact with the knowledge.
Knowledge Representation
(4) and Reasoning
We need an efficient way ...
... to build ontologies (5) Semantic Lifting
representing complex
knowledge domains. Storing and Accessing
(6) Semantic Data
... a way to reason about
knowledge.
www.iks-project.eu Copyright IKS Consortium
4. Page: 4
Outline
Ontologies
Ontology Design
Transformation and Refactoring
Using ontology networks in CMS platforms
www.iks-project.eu Copyright IKS Consortium
6. Page: 6
Computational ontologies
Ontologies are (software) components, expressed and
managed in standard W3C languages like RDF, OWL,
RIF, SPARQL
Computational Ontologies are artifacts
Have a structure (linguistic, logical, etc.)
Their function is to “encode” a description of the
world (actual, possible, counterfactual, impossible,
desired, etc.) for some purpose
www.iks-project.eu Copyright IKS Consortium
7. Page: 7
Computational ontologies
Ontologies must match both domain and task
They allow the description of the entities (“domain”) whose
attributes and relations are concerned because of some
purpose
social events and agents as entities that are considered in
a legal case
research topics as entities that are dealt with by a project,
worked on by academic staff, and can be topics of
documents
Serve a purpose (“task”)
finding entities that are considered in a same legal case
finding people that work on a same topic
matching project topics to staff competencies, time left,
available funds, etc.
www.iks-project.eu Copyright IKS Consortium
8. Page: 8
Two kinds of ontologies
Coverage-oriented ontologies
They cover the terminology/metadata/textual corpora/folksonomies ...
that fit a specific domain [big reengineering problem - exploited for
annotation, retrieval, etc.]
Task-oriented ontologies
They are able to give a structure to a knowledge base that can be
used to answer competency questions [big design and reuse
problem - exploited for automated reasoning and querying]
Currently
a mass of heterogeneous data and ontologies, either expressed or
portable to RDF (DB lifting, rdf-ized sources, etc.)
with generally low quality in some quality dimension/aspect
www.iks-project.eu Copyright IKS Consortium
9. Page: 9
Searching for ontologies on
the Semantic Web
www.iks-project.eu Copyright IKS Consortium
11. Page: 11
What can we do with OWL?
... (maybe) we can check the consistency, classify, and
query all this knowledge
this is great, but ...
... remember the Scarlet example
City subClassOf Country
Logical consistency is not the main problem
e.g. owl:sameAs can be wrongly used and still we
have consistency
Why OWL is not enough?
www.iks-project.eu Copyright IKS Consortium
12. Page: 12
When to use owl:Individual, owl:Class,
owl:ObjectProperty, owl:DatatypeProperty?
OWL gives us logical language constructs, but does not
give us any guidelines on how to use them in order to
solve our tasks.
E.g. modeling something as an individual, a class, or an
object property can be quite arbitrary
www.iks-project.eu Copyright IKS Consortium
13. Page: 13
New problems arising on the Web...
cf. Semantic Web Interest Group post May 27th, 2008 by Zille Huma:
"I have been wondering for sometime now that why isn't it a popular trend to store standard activities of a domain in
the ontology and not only the concepts, e.g., for the tourism domain, ontologies normally contain concepts like
Tourist, Resort, etc. but I have not so far come across an ontology that also contains the standard activities like
searchResort, bookHotel, etc. Why is it so? What support is provided in the ontology langauges to model the
standard activities of the domain as well?"
(1) “searching resorts is a type of functionality required for this kind of services”
owl:Class(searchResort) rdfs:subClassOf(Functionality)
(2) “a functionality for searching resorts is implemented in our web service”
owl:Individual(searchResort) rdf:type(Functionality)
(3) “who has been searching for what resorts in our web service?”
owl:ObjectProperty(searchResort) rdfs:domain(Customer) rdfs:range(Resort)
(4) “how many users have been using our resort searching functionality?”
owl:DatatypeProperty(searchResort) rdfs:domain(Customer)
rdfs:range(xsd:boolean)
www.iks-project.eu Copyright IKS Consortium
14. Page: 14
Solutions?
... OWL is not enough for building a good ontology, and
we cannot ask all web users either to learn logic, or to
study ontology design
Reusable solutions are described here as Ontology
Design Patterns, which help reducing arbitrariness
without asking for sophisticated skills ...
... provided that tools are built for any user
www.iks-project.eu Copyright IKS Consortium
16. Page: 16
A well-designed ontology ...
Obeys to “capital questions”:
What are we talking about?
Why do we want to talk about it?
Where to find reusable knowledge?
Do we have the resources to maintain it?
“Whats”, “whys” and “wheres” constitute the
Problem Space of an ontology project
Ontology designers need to find solutions from
a Solution Space
Matching problems to solutions is not trivial
www.iks-project.eu Copyright IKS Consortium
17. Page: 17
From the lessons learnt ...
Smallontologies with explicit documentation of
design rationales
components supported by specific functionalities
selection, matching, composition, etc.
implemented in repositories, registries, catalogues,
open discussion and evaluation forums, and in new-
generation ontology design tools
ontologydesignpattern.org
ODP and Watson APIs
NeOn ODP Plugin
etc.
www.iks-project.eu Copyright IKS Consortium
18. Page: 18
Ontology Design Patterns
An ontology design
pattern is a reusable
successful solution to a
recurrent modeling
problem
www.iks-project.eu Copyright IKS Consortium
20. Page: 20
General Content ODPs
Classification
Roles of objects
Containment
Part-whole relationships
Membership
Information and its realizations
Time and Places
Situation
Description
www.iks-project.eu Copyright IKS Consortium
21. Page: 21
Roles of objects
Objects can play different roles in different situations
Depending on the constraints given by the
requirements, modeling of objects and their roles can
be addressed differently
Do we want to represent properties of roles?
Do we want to classify objects based on their roles?
Do we want to assert facts about roles?
www.iks-project.eu Copyright IKS Consortium
22. Page: 22
Roles of objects
A beer mug used as vase
Books used as table’s legs
A sax player (person)
A song writer (person)
www.iks-project.eu Copyright IKS Consortium
23. Page: 23
Roles as classes
www.iks-project.eu Copyright IKS Consortium
24. Page: 24
Roles as classes
An object and its roles are related through the rdf:type
property
rdf:type relations can be either asserted or inferred
through classification
In order to automatically classify individuals in a certain
class the ontology has to define appropriate axioms
www.iks-project.eu Copyright IKS Consortium
25. Page: 25
Roles as classes
Consequences
Low expressivity
Roles are described at TBox level
Class taxonomy is bigger - a class for each role
Class taxonomy is entangled - multi-typing
ABox is smaller – same individual, several (role) types
Automatic classification of individuals through rdfs:subClassOf inheritance – with
proper axioms
Roles cannot be indexed in terms of space and time
Facts about roles cannot be expressed e.g. “Roles in UniBo can be student,
professor, researcher”, “Valentina is teacher for KMDM course”
Queries: ?x a SongWriter
General CQs
What objects have a (role) type?
www.iks-project.eu Copyright IKS Consortium
26. Page: 26
Roles as individuals
www.iks-project.eu Copyright IKS Consortium
27. Page: 27
Roles as individuals
An object and its roles are related through domain-
specific relations
Relations between an object and its roles have to be
asserted
Automatic inference of relations between an object and
its roles can be obtained through property sub-sumption
www.iks-project.eu Copyright IKS Consortium
28. Page: 28
Roles as individuals
Consequences
Expressivity is improved
Roles are described at ABox level
Class taxonomy is smaller – roles are individuals
Abox is bigger
Facts on roles can be asserted
Roles can be indexed in terms of time and space - through n-ary relations
N-ary relations are needed for relating an object to its role with respect to some other
object e.g. Valentina is teacher for KMDM course
kmdm_teacher involvesPerson Valentina
kmdm_teacher involvesRole teacher
kmdm_teacher involvesCourse KMDM
Valentina hasRole teacher
Roles do not type objects, no automatic classification of objects
Queries: ?x hasRole ?y ; ?x a Role
General CQs
What roles has an object? What objects have a role?
www.iks-project.eu Copyright IKS Consortium
29. Page: 29
Roles as properties
www.iks-project.eu Copyright IKS Consortium
30. Page: 30
Roles as properties
The semantics of “having a role” is embedded in the
name of a property
Typically properties conveying a role information are
verbs
Objects are not explicitly related to their roles, they are
related to other things through a property expressing an
action they perform, a role they play
Most common pattern in the web of data for modeling
roles
www.iks-project.eu Copyright IKS Consortium
31. Page: 31
Roles as properties
Consequences
Smaller taxonomy of classes
Bigger taxonomy of properties – a property for each role
Simpler graph of data – one triple for “Valentina is teacher for
KMDM course”
Valentina teaches KMDM
Roles cannot be indexed in terms of space and time
Semantics of roles is implicit (embedded in a property name)
Facts about roles cannot be expressed
Queries: ?x teaches ?y
General CQs
Who did something?
www.iks-project.eu Copyright IKS Consortium
32. Page: 32
Roles of objects
The
three solutions differ in expressivity, simplicity, and
CQs
Simplest is roles as properties
Most expressive is roles as individuals
Least expressive is roles as classes
Each of them has pros and cons
The choice depends on requirements
What about combining them?
www.iks-project.eu Copyright IKS Consortium
33. Page: 33
Combining roles as instances
with roles as classes
A class Role
A class for each Role e.g. SaxPlayer
A property restriction on classes representing roles, for
automatic classification
www.iks-project.eu Copyright IKS Consortium
34. Page: 34
Combining roles as instances
with roles as classes
Inthis example John_Coltrane is a Person
He has the role of sax_player
The property restriction on SaxPlayer allows to classify
John_Coltrane as a SaxPlayer
www.iks-project.eu Copyright IKS Consortium
35. Page: 35
…and add roles as properties
Note the restriction on property writerOf
www.iks-project.eu Copyright IKS Consortium
36. Page: 36
Indexing roles in terms of
time and space
www.iks-project.eu Copyright IKS Consortium
37. Page: 37
Indexing roles in terms of
time and space
www.iks-project.eu Copyright IKS Consortium
38. Page: 38
Content ODPs for roles of
objects
Object-Role
OWL pattern representing roles as individuals
http://ontologydesignpatterns.org/cp/owl/dul/objectrole.owl
Classes as roles
Sample pattern (template) representing roles as classes
http://ontologydesignpatterns.org/cp/owl/classesasrole.owl
Time-place-indexed-object-role
N-ary relation representing an objects, the roles it plays at
a certain date in a certain place
http://www.ontologydesignpatterns.org/cp/owl/dul/timeplaceindexedobjectrol
e.owl
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Object classification
The object-role relation can be generalized
An object is classified by a concept
Project hasStatus Status
IKS hasStatus active
Person hasRole Role
John_Coltrane hasRole sax_player
Object hasType Type
MacBookPro hasType laptop
Color hasParameter Parameter
Black hasParameter positive
Document hasTopic Topic
http://en.wikipedia.org/wiki/Paris hasTopic
http://dbpedia.org/resource/Paris
Action addresses Task
email_to_partners addresses meeting_notification
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Object classification
The discussion on modeling objects and their roles
holds for any classification relation
The general pattern is called “classification”
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Parthood
Objects can have different parts
Parthood relation is intuitively similar to set inclusion
The parthood relation is
Reflexive
ex:obj1 partOf ex:obj1
Anti-symmetric
ex:obj1 partOf ex:obj2 ∧
ex:obj2 part of ex:obj1
ex:obj1 owl:sameAs ex:obj2
Transitive
ex:obj1 partOf ex:obj2 ∧
ex:obj2 part of ex:obj3
ex:obj1 partOf ex:obj3
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Parthood modeling
Depending on the constraints given by the
requirements, modeling of objects and their parts can
be addressed differently
Do we want to distinguish parts of an object at different
points in time?
Do we want to distinguish parts of a whole from its
direct parts?
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Parthood examples
The CPU is part of the motherboard
The motherboard is part of the computer
partOf
partOf
partOf
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Parthood examples
The CPU is part of the motherboard
The motherboard is part of the computer
Time 2
partOf
Time 1
partOf
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Parthood examples
The CPU is direct part of the motherboard
The motherboard is part of the computer
partOf
directPartOf
directPartOf
partOf
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Part of
Note that reflexivity and anti-symmetry are not modeled
Anti-symmetry is not representable in OWL
Reflexivity would cause the materialization of numerous
somehow redundant triples, possibly not desirable in
LOD
it can be easily added as local axiom
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Direct part
directPartOf does not inherit transitivity
directPartOf implies part of
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Time-indexed parthood
We need an n-ary relation
Transitivity for time-indexed parthood is not expressible in
OWL
The range of datatype properties is not set, it depends on
local needs
Datatypes are all disjoint with each other e.g. xsd:date
owl:disjointWith xsd:gYear
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Time indexed parthood
…and if we want to have time intervals in our domain of
discourse?
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Time Interval
hasDate allows us to set any date within the time
interval
The range of the datatype properties is set locally
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Time indexed parthood with time
intervals
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Membership
Objects
can belong to a collection of objects
Membership is similar the notion of set membership
The membership relation is not transitive
if an object O belongs to a collection C, which in turn
belongs to a collection D, O is not said to belong to D
The membership relation chained with the parthood
relation implies parthood
if an object O belongs to a collection C, which in turn is
part of a collection D, O belongs to D
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Membership modeling
Depending on the constraints given by the
requirements, modeling of collections and their
members can be addressed differently
Do we want to identify members of a collection at a
certain point in time?
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Membership
Note the property chain of memberOf and partOf that
implies memberOf
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Time indexed membership
Analogously to parthood, we can have the variant with
time intervals
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Containment
Parthood and membership can be generalized as containment relations
Containment relation represents a general cognitive schema
A containment schema involves a physical or metaphorical
boundary
enclosed area or volume, or
excluded area or volume
A containment schema can have additional optional properties, such as
transitivity of enclosure (whereby if one object is enclosed by a second, and that by a
third, the first is also enclosed by the third)
objects inside or outside the boundary
protectedness of an enclosed object
the restriction of forces inside the enclosure, and
the relatively fixed position of an enclosed object
We model containment as a general transitive relation between objects
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Containment
Time indexed containment and time indexed
containment with time intervals are analogous to the
previous ones (time indexed membership and parthood
with/without time intervals).
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Information objects and their
realizations
An information object is a piece of information
independently from how it is concretely realized
Examples of information objects are musical
compositions, texts, words, pictures, etc.
An information realization is a concrete realization of an
Information object
Examples of information realization are the written
document containing the text of a law, an mp3 file, etc.
The distinction between information objects and their
realizations is a key requirements in copyright
management
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Modeling Information Objects
Depending on the constraints given by the
requirements, modeling information objects and their
realizations can be addressed differently
Do we want to temporarily index the realization of an
information object?
Do we want to spatially index the realization of an
information object?
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Example of information objects
and their realizations
L’inferno
isRealizedBy
della divina
commedia di
Dante
isRealizedBy
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Examples of information objects
and their realizations
Pictures of a party are realized in JPG files
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Examples of information objects
and their realizations
The realizations of pictures of the party are available on
my laptop at the moment I download the attachments to
this email
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Time and Place indexed
information realization
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Situation
A general vocabulary for n-ary relations
Situation is able to represent reified n-ary relations, by
defining a top-level relation for all binary projections of
the n-ary relation
A way somebody conceives a state of affairs, a set of
things, a fact
All time indexed (and place indexed) patterns we have
seen so far are (in principle) subclass of Situation
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Description
A Description is represents a conceptualization
It can be thought also as a 'descriptive context'
It uses or defines concepts in order to create a view on
a 'relational context' (Situation) out of a set of data or
observations
Examples of descriptions are:
a Plan of some actions to be executed by agents in a
certain way, with certain parameters;
a Diagnosis that provides an interpretation for a set of
observed entities
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Description and Situation
A situationis a view, consistent with ('satisfying') a
description, on a set of entities.
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Example of description and
situation
A workflow is a description, its execution is a situation
satisfying it
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