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Formal Ontology and Information Systems

Formal Ontology and Information Systems. Nicola Guarino (FOIS’98) Presenter: Yihong Ding CS652 Spring 2004. Ontologies are important. Knowledge engineering Knowledge representation Language engineering Qualitative modeling Information modeling

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Formal Ontology and Information Systems

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  1. Formal Ontology and Information Systems Nicola Guarino (FOIS’98) Presenter: Yihong Ding CS652 Spring 2004

  2. Ontologies are important • Knowledge engineering • Knowledge representation • Language engineering • Qualitative modeling • Information modeling • Information retrieval, extraction, and integration • Object-oriented analysis • Knowledge management and organization • Database design • Agent-based system design • The Semantic Web

  3. e-Business requests “Trying to engage with too many partners too fast is one of the main reasons that so many online market makers have foundered. The transactions they had viewed as simple and routine actually involved many subtle distinctions in terminology and meaning” Harvard Business Review, October 2001

  4. Technical problem for e-Business requests “Lack of technologies and products to dynamically mediate discrepancies in business semantics will limit the adoption of advanced Web services for large public communities whose participants have disparate business processes” Gartner Research, February 28, 2002

  5. XML is not the solution “XML is only the first step to ensuring that computers can communicate freely. XML is an alphabet for computers and as everyone who travels in Europe knows, knowing the alphabet doesn’t mean you can speak Italian or French” Business Week, March 18, 2002

  6. Open- and closed-world assumptions • Closed-world assumption • The information provided is complete (a knowledge base contains all relevant facts). • Known the knowledge base is incomplete (does not have enough information to produce an answer to a question), a decision must be made without complete information. • If you cannot prove P or not P, assume it is false. • This is the usual semantics of relational databases. • The closed-world assumption is designed to finesse but not solve these problems and is adopted in default of a better solution.

  7. Open- and closed-world assumptions • Open-world assumption • Any proposition or theorem which cannot be derived from the facts and axioms present in the system is held to be unknown. • Things which are known to be true or false must be stated explicitly, or else inferable from facts and axioms. • The two boolean values (true and false) are inadequate, and we have to use the ThreeValuedLogic. • The open-world assumption more clearly models reality. • The number of domains can be infinite.

  8. Schema: Closed-world assumption Car Car Make Model has 001 Ford Taurus make has 002 Honda Accord … … … model … … … … … … Example: from schema to ontology Local Database

  9. Example: from schema to ontology Ontology: Open-world assumption Automobile make and model definition Automobile definition Car … Model … Model has … Taurus … f-150 make has … Accord … Civic … … … … www.cars.com Car makes and models model … … … … … … … …

  10. What is an ontology? • “An ontology is a formal, explicit specification of a sharedconceptualization.” [Gruber 93] • Formal • The ontology should be machine readable. • Explicit • The type of concepts use, and the constraints of their use are explicitly defined. • Shared • The ontology should capture consensual knowledge accepted by the communities. • Conceptualization • An ontology is an abstract model of phenomena in the world by having identified the relevant concepts of those phenomena.

  11. apple LE same conceptualization mela LI What is a conceptualization? • Formal structure of (a piece of) reality as perceived and organized by an agent, independently of: • the vocabulary used • the actual occurrence of a specific situation

  12. Scene 1: blocks on a table Scene 2: a different arrangement Conceptualization Conceptualization of scene 1: <{a, b, c, d, e }, {on, above, clear, table }>

  13. n D r 2 Î n n D r 2 ® : W n Relations vs. Conceptual Relations ordinary (extensional) relations are defined on a domain D: conceptual (intensional) relations are defined on a domain space<D, W> • A conceptualization is a set of conceptual relations defined on a domain space.

  14. Intended Model and Ontological Commitment • World structure: a structure of <D, R>, which refers to a particular world • Intended world structure: a world structure for a conceptualization in a particular world • Each conceptualization contains many of them. • One intended world structure for each world. • Intended model: the representation of an intended world structure in a model by ontological commitment. • Ontological commitment: the intensional interpretation of a logical language L

  15. Conceptualization Commitment: K = < C,I> Language L Models MD(L) Interpretation Intended models IK(L) Ontologies and Intended Models Ontology

  16. Bad Good WORSE Ontology Quality imprecision incompleteness

  17. The Ontology Integration/Sharing Problem (1) Agents A and B can communicate only if their intended models overlap

  18. The Ontology Integration/Sharing Problem (2) Two different ontologies may overlap while their intended models do not (especially if the ontologies are not accurate enough)

  19. ITOP(L) M(L) False agreement! The role of foundational ontologies (1) IB(L) False agreement minimized IA(L)

  20. The role of foundational ontologies (2) • Bottom-up integration of domain-specific ontologies can never guarantee consistency of intended models (despite apparent logical consistency). • Top-level foundational ontologies • Simplify domain-specific ontology design • Increase quality and understandability • Encourage reuse

  21. Hierarchies of Ontologies

  22. Towards Ontology-Driven IS: temporal dimension (1) Using an ontology at development time • Benefit • Enable knowledge reuse instead of software reuse • Enable application domain knowledge reuse and share across heterogeneous software platforms • Avoid bothering too much on implementation details • Two scenarios • First scenario: has ontology library containing reusable domain and task ontologies • Second scenario: has very generic ontology consisting of coarse domain-level distinctions

  23. Towards Ontology-Driven IS: temporal dimension (2) Using an ontology at run time • Benefit • Enable communication between software agents • Two cases • Ontology-aware IS: an IS component is just aware of the existence of a (possibly remote) ontology and can query it for whatever specific application purpose • Ontology-driven IS: the ontology is just another component (typically local to the IS), cooperating at run time towards the “higher” overall IS goal

  24. Towards Ontology-Driven IS: structural dimension • Ontology as a database component • Ontology as a user interface component • Ontology as an application program component

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