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Grounding Ontologies with Social Processes and Natural Language 2012-04-26 IFIP WG 12.7 Workshop #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.].
+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
+Tri-sortal Network of 3 Networks of Actors
+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
+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
+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
+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)
+DOGMA
“Double Articulation”: Ontological Commitments in DOGMA
Lexon Base Commitment Layer Applications
+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)
+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
+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
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Semantic Interoperation of IS through Formalized Social Processes
Method
Implementation of the ontology
E.g., with tools offered by the RDB2RDF community such as D2R Server.
OWL, RDF(S), …
+Lexons + Constraints
+Method
ManageCommunity
Manage Semantic Interoperability Requirements
Articulate with glosses
Create Lexons
Constrain Lexons Commit
Gloss-Equivalence Synonym
+Discussion oriented + Traceability
+Exploiting the annotated data (in RDF)
+Gloss Driven!
+Joint work with CVC on Ω and MTB Co-evolution
+Exploiting RDF thanks to Hybrid Ontology Implementations
n Augmenting RDB2RDF Mappings by means of Ω-RIDL Commitments
n Adding semantics to the database structure
+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))