2012 04-26-ifip-wg.pptx

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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))