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Working with Ontologies

Working with Ontologies. Introduction to DOGMA and related research. Outline. Ontology DOGMA Semantic Web Issues. Ontology Definition. “Classical” definition: “Specification of a conceptualization” Keyword: Agreement Semantic consistency Unambiguous communication. Ontology Paradigms.

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Working with Ontologies

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  1. Working with Ontologies Introduction to DOGMA and related research

  2. Outline • Ontology • DOGMA • Semantic Web • Issues

  3. Ontology Definition • “Classical” definition: “Specification of a conceptualization” • Keyword: Agreement • Semantic consistency • Unambiguous communication

  4. Ontology Paradigms • Logic • A priori specification • Formal logic • Necessarily Small-scale • Modeling • Focus on application • Formal basis • Potentially large-scale

  5. Ontology Paradigms • Extensional vs. Intensional • Intensional • Strongly based on axioms and rules • Hard agreement • Extensional • Large collections of facts • Scalablility

  6. designer interpretation Any Design Tool domain expert Implementation “World” ONTOLOGY agreement Conceptual Schema Information System (including the WWW) user Data Ontology and IS Semantics

  7. Ontology Grail “specification of interface, communication and documentation for any module in any software system is mapable to a common ontology” [Meersman 2000]

  8. Outline • Ontology • DOGMA • Semantic Web • Issues

  9. DOGMA Purpose • STARLab Ontology experimentation platform • Flexible, modular architecture • Lexon-based metamodel • Ontology Server generator

  10. DOGMA Architecture

  11. DOGMA Metamodel • Lexons: elements of form g <t0r t> where g is a context; t0, t are terms and r is a role

  12. DOGMA metamodel • Example: (#my_company)employee is_a (#living_being)person is_a contract_party WITH first_name WITH last_name WITH empl-id has_birth date has_start date has salary works_in department

  13. DOGMA metamodel

  14. DOGMA Syntax • XML-based representation of the model. • Bulk conversion of ontologies: • Conversion of existing ontology to DOGMA syntax • Bulk insertion in a separate context • (Semi-)Manual alignment

  15. DOGMA API • Programmatic access to the ontology for clients • Java 2 API • Direct support of the metamodel • Basic operations support

  16. DOGMA Content • Incorporation of well-known thesaurus • WordNet • Project-specific content] • EuroWordnet base types • IPTC Category System • ….

  17. DOGMA Applications • Generation of application-specific “views” on the global ontology • Delivery of support applications • (Tailored) Browsers/Editors • DOGMA Projects: • Hypermuseum • NAMIC

  18. DOGMA Applications: HM • Hypermuseum project • Purpose: To create a tool for the creation of websites to browse of museum information • Ontology-supported navigation and searching of appropriate museum data • Ontology sources: • Models from museums • Data from museums • WordNet

  19. DOGMA Applications: NAMIC • News processing project • Purpose: Support of journalists in news agencies • Project-wide ontology-based semantics • Ontology service • User profiling

  20. DOGMA Applications: NAMIC

  21. DOGMA Applications: NAMIC • Merged ontological resources • News categories (IPTC) • Lexical resources • EuroWordNet • Named Entities • User profiling • Determine the user’s information needs • Provide a consistent view of the system for developers and users

  22. Outline • Ontology • DOGMA • Semantic Web • Issues

  23. Semantic Web Introduction • “The Web was designed as an information space, with the goal that it should be useful not only for human-human communication, but also that machines would be able to participate and help. One of the major obstacles to this has been the fact that most information on the Web is designed for human consumption […] the Semantic Web approach instead develops languages for expressing information in a machine processable form.” http://www.w3.org/DesignIssues/Semantic.html

  24. Semantic Web Syntactic level • XML: General syntactic infrastructure • Arbitrary document types defined by DTD (or XML Schema) • Related standards • Namespaces • Linking • ….

  25. Semantic Web Vocabulary level • RDF(S) • Topic Maps

  26. Semantic Web Vocabulary level

  27. Semantic Web Vocabulary level <rdf:RDF> <rdf:Description about="http://mycollege.edu/courses/6.001"> <s:students> <rdf:Bag> <rdf:li resource="http://mycollege.edu/students/Amy"/> <rdf:li resource="http://mycollege.edu/students/Tim"/> <rdf:li resource="http://mycollege.edu/students/John"/> <rdf:li resource="http://mycollege.edu/students/Mary"/> <rdf:li resource="http://mycollege.edu/students/Sue"/> </rdf:Bag> </s:students> </rdf:Description> </rdf:RDF>

  28. Semantic Web Vocabulary level • RDF Schema • Classes and properties • Constrains • Extensibility

  29. Semantic Web Vocabulary level

  30. Semantic Web Logical level • Very much in progress • Some prototype languages and systems • Fundamental scalability problems

  31. Semantic Web and DOGMA • Similar assertion-based metamodels • Possibility of using DOGMA as a repository for Ontologies in the Semantic Web

  32. Outline • Ontology • DOGMA • Semantic Web • Issues

  33. Future work • Alignment • Visualization • Mining • Semantic Web Convergence

  34. Alignment concepts • Merging: To create a single coherent ontology that includes all the information form all sources • Alignment:To make the all sources consistent and coherent with one another but keep them separate

  35. Alignment algorithms • PROMPT: Semiautomatic, semantic-based algorithm • Simple frame-based knowledge model: • Classes • Slots • Facets • Instances

  36. Alignment algorithms: PROMPT Make initial suggestions Select next operation Perform automatic updates Find conflicts Make suggestions

  37. Alignment algorithms: PROMPT

  38. Alignment algorithms: PROMPT

  39. Mining • Content availability is a major issue • Sources: • Conceptual schemas • Database schemas • XML DTD’s and schemas • Semantic web • ….

  40. Issues and DOGMA • Aligment: Direct support (and better algorithms) needed • Mining: DOGMA model allows quick incorporation of new ontology data • Visualization: Potential large-scale ontologies may require new techniques

  41. Projects available! http://starlab.vub.ac.be

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