The document discusses the definition and significance of ontologies in computer science, focusing on their role in establishing shared meanings among autonomous systems while facilitating interoperability. It emphasizes the dual perspectives of human and system reasoning, and the need for community involvement in ontology development through methods such as discourse analysis and legacy data utilization. The document also explores hybrid ontology engineering and the integration of natural language processing to enhance semantic interoperation.

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

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Tri-sortal Network of 3 Networks of
Actors

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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

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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)

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DOGMA
“Double Articulation”: Ontological Commitments in DOGMA
Lexon Base
Commitment Layer
Applications

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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 through
Formalized Social Processes
03/21/12 15

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Lexons + Constraints

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Method
Manage Articulate Create Constrain
Community Commit
with glosses Lexons Lexons
Manage Semantic
Interoperability Gloss-
Synonym
Requirements Equivalence

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Discussion oriented + Traceability

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Exploiting the annotated data
(in RDF)

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Gloss Driven!

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Joint work with CVC on Ω and MTB
Co-evolution

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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)) }