Your SlideShare is downloading. ×
  • Like
2012 04-26-ifip-wg.pptx
Upcoming SlideShare
Loading in...5

Thanks for flagging this SlideShare!

Oops! An error has occurred.


Now you can save presentations on your phone or tablet

Available for both IPhone and Android

Text the download link to your phone

Standard text messaging rates apply

2012 04-26-ifip-wg.pptx


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 …

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).

Published in Education
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
No Downloads


Total Views
On SlideShare
From Embeds
Number of Embeds



Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

    No notes for slide


  • 1. + Grounding Ontologies with Social Processes and Natural Language 2012-04-26 IFIP WG 12.7 Workshop #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. + 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. + Tri-sortal Network of 3 Networks of Actors
  • 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. + 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. + 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. + 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. + DOGMA“Double Articulation”: Ontological Commitments in DOGMA Lexon Base Commitment Layer Applications
  • 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. + 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. + 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. + 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. + Lexons + Constraints
  • 15. + Method Manage Articulate Create Constrain Community Commit with glosses Lexons Lexons Manage Semantic Interoperability Gloss- Synonym Requirements Equivalence
  • 16. + Discussion oriented + Traceability
  • 17. + Exploiting the annotated data (in RDF)
  • 18. + Gloss Driven!
  • 19. + Joint work with CVC on Ω and MTB Co-evolution
  • 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. + 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)) }