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OIL: An Ontology Infrastructure for the Semantic Web

OIL: An Ontology Infrastructure for the Semantic Web. D. Fensel, F. van Harmelen, I. Horrocks, D. L. McGuinness, P. F. Patel-Schneider Presenter: Cristina Nicolae. Ontologies. “An ontology is a formal , explicit specification of a shared conceptualization .”

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OIL: An Ontology Infrastructure for the Semantic Web

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  1. OIL: An Ontology Infrastructure for the Semantic Web D. Fensel, F. van Harmelen, I. Horrocks, D. L. McGuinness, P. F. Patel-Schneider Presenter: Cristina Nicolae

  2. Ontologies • “An ontology is a formal, explicit specification of a sharedconceptualization.” • conceptualization: abstract model of some phenomenon in the world that identifies that phenomenon’s relevant concepts • explicit: the type of concepts used and the constraints on their use are explicitly defined • formal: the ontology should be machine understandable • shared: an ontology captures consensual knowledge (accepted by a group)

  3. Applications of ontology technology (1/3) • Knowledge management • acquiring, maintaining and accessing an organization’s knowledge • weaknesses: • searching information (irrelevant word in other context) • extracting information (lack commonsense knowledge) • maintaining (large sources) • automatic document generation (require a machine-accessible representation of the semantics of info sources) • future solution: • semantic annotations

  4. Applications of ontology technology (2/3) • Web commerce • online stores, shopping agents, online marketplaces, auction houses • get information from several stores through wrappers – which use keyword search to find product info • limitations: • effort (writing wrapper for each online store is time-consuming + changes in store) • quality (info extracted is limited, error-prone and incomplete) • future solution: • software agents to understand product information

  5. Applications of ontology technology (3/3) • Electronic business • e-commerce in business-to-business field • protocol (standard): the UN Edifact • shortcomings: • procedural and cumbersome standard • programming of business transactions expensive and error-prone • large maintenance efforts • an isolated standard • future solution: • using the Internet’s infrastructure for business exchange

  6. OIL • HTML: initial, simplistic • XML: provides serialized syntax for trees • RDF: defines a syntactical convention and a simple data model – triples: object/property/value • RDF Schema: introduces basic ontological primitives into the Web – classes, subclasses, subproperties, restrictions.. • OIL: based on RDFS, enriched into a full-fledged Web-based ontology language

  7. Criteria that OIL matches • We need an advanced ontology language to express and represent ontologies. Must be: • highly intuitive to the human: • OIL frame-based • central modeling primitives are classes (frames) with attributes • well-defined formal semantics (completeness, correctness and efficiency) • OIL description logics • knowledge is described in terms of concepts and role restrictions • proper link to existing Web languages (XML, RDF) • OIL  syntax in XML, based on RDF • a standardized syntax for writing ontologies and a standard set of modeling primitives

  8. OIL’s layered architecture • Each layer adds functionality and complexity to the previous one • Core OIL: coincides with RDFS except reification features • Standard OIL: specifying the semantics and making complete inferences viable • Instance OIL: full-fledged database capability • Heavy OIL: will include additional representational and reasoning capabilities

  9. An ontology

  10. OIL tools • Ontology editors • build new ontologies • OntoEdit (U. Karlsruhe), OILed (U. Manchester), Protégé (Stanford) • Ontology-based annotation tools • we can derive an XML DTD and an XML Schema definition from an ontology in OIL • we can derive an RDF and RDFS definition for instances from OIL • Reasoning with ontologies • reason about an ontology’s instances and schema definition • FaCT

  11. Applications of OIL • Swiss Life: Organizational memory • an intranet-based front end to an organizational memory • British Telecom: Call centers • call center agents use a variety of electronic sources for information when interacting with customers  OIL provides front end tool • EnerSearch: Virtual enterprise • is a virtual organization researching new IT-based business strategies and customer services in deregulated energy markets  OIL toolkit enhances knowledge transfer.

  12. Conclusions on OIL • is properly grounded in Web languages (XML Schemas & RDFS) • inner layers enable efficient reasoning support based on FaCT • has a well-defined formal semantics

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