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Integrating Ontology Storage and Ontology-based Applications

Peter Mika:. Integrating Ontology Storage and Ontology-based Applications. A lesson for better evaluation methodology. Contents. The context: A case study in ontology-based knowledge management The problem area What did we find? Some observations The solution and the lessons learned

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Integrating Ontology Storage and Ontology-based Applications

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  1. Peter Mika: Integrating Ontology Storage and Ontology-based Applications A lesson for better evaluation methodology EON 2002

  2. Contents • The context: A case study in ontology-based knowledge management • The problem area • What did we find? Some observations • The solution and the lessons learned • Significance for evaluation methodology EON 2002

  3. Context of the development • On-To-Knowledge • Contributions include • OIL, • Methodology for ontology-based KM, • Tools for the extraction, editing, sharing, storage and control of ontologies, plus ontology-based reasoning, search, navigation and ontology visualization, • Case studies • No single product, only a loose-coupling between tools • The EnerSearch case • Virtual organization which disseminates scientific content through its website • Goal: improved access to information through ontology-based search and navigation • Technology and user-focused evaluation of the On-To-Knowledge approach: Does integration through loose coupling really work? EON 2002

  4. The integration Spectacle Server • Ontology storage • The Sesame RDF storage and query facility • Ontology-based applications • Ontology-based portal generation with Spectacle • Ontology-based search engine using QuizRDF Publishing API Query Portal gen.app Results Sesame Query Results QuizRDF EON 2002

  5. Ontology storage architecture • Basic operations • Upload, extract, remove, clear • Query, browse • One of the functional modules is the RDF inference engine (RDF-MT) • Not only a storage facility any more! EON 2002

  6. Observations 1/2 • Small, frequent queries result in poor performance • Limited expressivity, inefficient evaluation for some queries • Transformations are required based on client-side semantics EON 2002

  7. Observations 2/2 • Server-side transformations are inefficient: • The larger the repository, the slower it is • Admin., communication costs are significant for small datasets • Transformations weigh heavily on storage functionality EON 2002

  8. The solution • Move query/transformation work from server to client where it results in greater efficiency • Mechanism for shipping query/transformation knowledge: Portable Inference Modules (PIM) • Wrapper for inference knowledge described in procedural form • Software module with a standardized interface, working on a predefined data model • Portable, self-descriptive, trusted EON 2002

  9. Applications • Use in On-To-Knowledge: • Simple queries/transformations on the client-side • Significantly improved performance, allowing applications to scale to the size of a fairly large ontology (~150,000 statements) • Future work • Extending Sesame with a PIM container • Enrich the expressivity of your ontology language in a plug-and-play fashion! • Compiling PIMs from declarative forms of rules EON 2002

  10. The evaluation methodology lesson • What did we see on the previous slides? • An ontology-based system built from loosely coupled components • Performance, scalability issues start to appear when integrating components • Missing pieces of the architecture • The message for web services developers: watch out! • Lesson for the On-To-Knowledge methodology for technology-focused evaluation: The evaluation of ontology-based tools should be complemented with the evaluation of frameworks or architectures of ontology-based systems. EON 2002

  11. Maybe next year… • Evaluation of Ontology-based Frameworks? (EOF) EON 2002

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