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Thomas Rölleke

HySpirit: Describing Knowledge Retrieval. Thomas Rölleke Research Fellow, Department of Computer Science, Queen Mary Unversity of London, Director HySpirit GmbH Research Seminar Talk Department of Computer Science, University of Essex Friday, 02. November 2001. Outline. Requirements.

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Thomas Rölleke

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  1. HySpirit: Describing Knowledge Retrieval Thomas Rölleke Research Fellow, Department of Computer Science, Queen Mary Unversity of London, Director HySpirit GmbH Research Seminar Talk Department of Computer Science, University of Essex Friday, 02. November 2001 DCS, University of Essex

  2. Outline DCS, University of Essex

  3. Requirements Methods,... • support searching • support semi-structured • (many-structured) data • gain new knowledge • be flexible, • effective, and “simple“ • free text • SQL, OQL, XML, etc. • tf-idf • page-rank: • a page is “good “ if... Intro descriptional approach ofHySpirit DCS, University of Essex

  4. Requirements of hypermedia and knowledge retrieval • uncertainty of knowledge relevance-based ranking • relationships, in particular: structure • knowledge extraction, step from a set of terms (words) to a set of complex propositions (relationship, classification) • media objects: description requires more than a set of words • querying: complex! different! application-dependent! user-dependent! APPROACH: make logic (powerful data models) applicable for retrieval, provide descriptional framework for retrieval, not only a black-box point-and-click solution DCS, University of Essex

  5. HySpirit knowledge modelling layers POOL: Propabilistic object-oriented logic FVPD: Four-valued probabilistic Datalog PD: Probabilistic Datalog PSQL: Probabilistic SQL PRA: Probabilistic relational algebra DCS, University of Essex

  6. POOL layer DCS, University of Essex

  7. PRA layer result = PROJECT[$3](JOIN[$1=$1] (qterm, term) 0.9 · 0.5 = 0.45 doc1 0.8 · 0.5 + 0.7 · 0.5 - 0.8 · 0.5 · 0.7 · 0.5 = 0.4 +0.35 - 0.14 = 0.61 doc2 DCS, University of Essex

  8. Probability estimation based on “probabilistic“ tupels Probability that term t occurs:P1(t) := freq(t)/sum_freq Inverse document frequency: idf(t) := -log P(t) Probability of term t to be “discriminative“: P2(t) := idf(t)/sum_idf Probability that document d occurs: P(d) := freq(d)/sum_freq Achievement: General probability estimation based on general data format DCS, University of Essex

  9. w_this doc1 doc1 w_doc1 0.9 0.7 sec11 sec12 sec11 sec12 w_sec11 w_sec12 0.8 sailing 0.6 boats Semantics of HySpirit based on possible world semantics (Kripke structures) Documents are considered as agents, accessibility relations model structure DCS, University of Essex

  10. cat(tom) tweety(bird) NOT tweety(bird) cat(tom) Structured document retrieval Knowledge “augmentation“: doc7[ bird(tweety) cat(tom) ] doc8[ NOT bird(tweety) ] ?- D[NOT bird(tweety)] (doc8) ?- D[NOT cat(tom)] ??? ?- cat(X) (tom) ?- bird(tweety) ??? DCS, University of Essex

  11. XML retrieval, MPEG7 retrieval M < > - DCS, University of Essex

  12. Demo DCS, University of Essex

  13. Summary • Requirements and methods for hypermedia and knowledge • retrieval introduced • Descriptional approach of HySpirit introduced • Layers: POOL, PRA • Applications: Structured document retrieval, XML retrieval, • MPEG7 retrieval • R&D around HySpirit aims at a flexible, effective and • “simple“ SDK for building complex search applications • With new technology new possibilities arise ... DCS, University of Essex

  14. References DCS, University of Essex

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