2012 04-26-ifip-wg.pptx


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Presentation on STARLab's research, the GOSPL method and prototype presented at the second IFIP WG12.7 "Social Networking Semantics and Collective Intelligence" workshop in Amsterdam (26-27 April 2012).

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2012 04-26-ifip-wg.pptx

  1. 1. + Grounding Ontologies with Social Processes and Natural Language 2012-04-26 IFIP WG 12.7 Workshop #2
  2. 2. + Definition of Ontology in Computer Science n  A conceptualization is a mathematical construct that contains abstract references to (1) objects, (2) relations, (3) functions, and (4) events as may be observed in a given real world. n  An ontology is a shared, [first order] logical, computer- stored, specification of such an agreed explicit conceptualization. n  [Tarski 1908, Gruber 1993, Studer 2000, et al.].
  3. 3. + Definition of Ontologies in Computer Science n  In summary: Semantics = Agreed Meaning n  Links symbols in autonomously developed systems to shared reality n  Agreed among humans as cognitive agents n  Stored in ontologies n  key technology for interoperability n  ontologies ≠ data models, but provide annotation for them n  support both human- and system-based reasoning
  4. 4. + Tri-sortal Network of 3 Networks of Actors
  5. 5. + Interoperation != Integration n  The autonomous nature of actors needs to be respected n  Interoperation stems from a need or wish to communicate, and collaborate n  à Motivates the need for agreements, contracts and the meaningful exchange of concepts
  6. 6. + The need for dual perspectives n  Human perspective: high level reasoning about “shared” concepts n  put humans “in the loop” n  natural language contexts n  System perspective : vocabulary agreements, lexons n  large volume data access n  low level reasoning
  7. 7. + Ontology Engineering Methods: Learning from Databases n  Technology matures: involve the less IT-gifted IT experts n  Natural language discourse analysis (NIAM, ORM) as used for databases n  Use legacy data / output reports / interviews, abstraction into fact types n  Lift data models into ontologies, remove application-specific context
  8. 8. + Developing Ontology-Grounded Methods and Applications n  Communities of users / domain experts own the ontology. Make use of discourse, social process and “legacy” resources n  Ontologies as approximations of perceived reality at type level! As ontologies evolve, they approximate the real world n  Users / domain experts rule at every step n  Facts holding in a certain context (the community, see later)
  9. 9. + DOGMA“Double Articulation”: Ontological Commitments in DOGMA Lexon Base Commitment Layer Applications
  10. 10. + Commitments in DOGMA n  Commitment = < Selection, Encoding, Constraints > n  Where Selection = set of lexons with various Context-ids n  Encoding = reference mapping: Application symbols to lexon terms n  Constraints = set of Ω-RIDL* statements (expressed in lexon terms)
  11. 11. + Towards Hybrid Ontology Engineering n  Revisit discourse analysis, pragmatics, semiotics n  Model communities as 1st class citizens n  Formalize methodologies based on NL involvement of domain experts à Revisit discourse analysis, pragmatics, semiotics n  Upgrading role of legacy systems in enterprises n  Scalable semantic re-exploitation of RDF and LOD resources
  12. 12. + Grounding Ontologies with Social Processes and Natural Language n  Hybrid Ontology Description (HOD) HΩ=<Ω,G> n  Ω is a DOGMA Ontology Description (Lexon base, commitments and a mapping from terms to concepts) n  The contexts in hybrid ontology descriptions communities n  G is a glossary, a triple with components n  Gloss, a set of linguistic, human-interpretable glosses. Mappings from community-term pairs or lexons to glosses
  13. 13. + Method  Implementation of the ontology OWL, RDF(S), …  E.g., with tools offered by the RDB2RDF community such as D2R Server.Semantic Interoperation of IS throughFormalized Social Processes03/21/12 15
  14. 14. + Lexons + Constraints
  15. 15. + Method Manage Articulate Create Constrain Community Commit with glosses Lexons Lexons Manage Semantic Interoperability Gloss- Synonym Requirements Equivalence
  16. 16. + Discussion oriented + Traceability
  17. 17. + Exploiting the annotated data (in RDF)
  18. 18. + Gloss Driven!
  19. 19. + Joint work with CVC on Ω and MTB Co-evolution
  20. 20. + Exploiting RDF thanks to Hybrid Ontology Implementations n  Augmenting RDB2RDF Mappings by means of Ω-RIDL Commitments n  Adding semantics to the database structure
  21. 21. + Exploiting RDF thanks to Hybrid Ontology Implementations n  Fact-oriented querying of RDF. n  LIST Artist NOT with Gender with Code = ‘M’ n  In SPARQL: SELECT DISTINCT ?a WHERE { ?a a myOnto0:Artist. OPTIONAL { ?g myOnto0:Gender_of_Artist ?a. ?g myOnto0:Gender_with_Code ?c. } FILTER(?c != "M" || !bound(?c)) }