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prof. dr. L. Schomaker KI/RuG

project I 2 RP Intelligent Information Retrieval and Presentation in public historical multimedia databases. prof. dr. L. Schomaker KI/RuG. ToKeN2000. grants for research between computer science, AI and cognitive science money from Min. of Econ. affairs and Min. of Education

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prof. dr. L. Schomaker KI/RuG

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  1. project I2RPIntelligent Information Retrieval and Presentationin public historical multimedia databases prof. dr. L. Schomaker KI/RuG

  2. ToKeN2000 • grants for research between computer science, AI and cognitive science • money from Min. of Econ. affairs and Min. of Education • demonstrating that the ‘human perspective’ has an added value • demonstrating that working systems and/or models can be implemented

  3. ToKeN2000

  4. I2RP partners • CWI • Universiteit Leiden • Universiteit Maastricht • Rijksuniversiteit Groningen • Rijksmuseum Amsterdam

  5. Supervisors + Rijksmuseum: dhr. K. Schoemaker

  6. Researchers + M.Sc. students

  7. Intelligent Information Retrieval and Presentation Information Retrieval: searching in weakly organized multimedial databases Presentation: user and context-related rendering of retrieved results “Intelligent”, i.e., making use of methods from AI and Cognitive Science

  8. Upper-left picture is the query • “boy in yellow raincoat” • …yields very counter-intuitive results •  What was the user’s intention?

  9. Human-machine communication • Grice’s Maxims of bi-directional cooperative dialog: • quantity (adapt the size of your answer) • quality (tell the useful truth) • relation (react to what has been asked) • manner (avoid ambiguities) • Current HMC violates most of these maxims

  10. Starting points in I2RP • Bidirectional cooperative dialog (Grice) (maxims of quantity, quality, relation, manner) • An example of ‘intelligent information retrieval and presentation’: car sales Buyer: “I’m looking for a Volvo 850 Estate for less than 5000 Euro”

  11. Starting points in I2RP • Bidirectional cooperative dialog (Grice) (maxims of quantity, quality, relation, manner) • An example of ‘intelligent information retrieval and presentation’: car sales Buyer: “I’m looking for a Volvo 850 Estate for less than 5000 Euro” Seller: “we don’t have it” (logical response)

  12. Starting points in I2RP • Bidirectional cooperative dialog (Grice) (maxims of quantity, quality, relation, manner) • An example of ‘intelligent information retrieval and presentation’: car sales Buyer: “I’m looking for a Volvo 850 Estate for less than 5000 Euro” Seller: “we don’t have it” (logical response) vs Seller: “we do have a Mitsubishi Station of 5500 Euro” (intelligent response)

  13. Reasoning with world knowledge all cars (2) family car! sports cars SUVs (1) Volvo 850 Estate (3) Mitsubishi Station

  14. Knowledge sources in I2RP • A bi-directional cooperative dialog (Grice)… • Requires: world knowledge  semantic web, ontologies knowledge on humans  user modeling, language

  15. Project Partners • Optima: A user agent for object-based image search • Spreekbuis: A Dutch sentence generator • Cuypers: Automatic user-centric hypermedia generation • GO: Graphical Ontologies

  16. Spreekbuis: a sentence generator for Dutch • UL (C. van Breugel/Arsenijevic) • Performance Grammar Workbench (PGW)

  17. KI RuG Optima: a user agent for object-based image search • KI/RuG, Taatgen/Grob/Schomaker • User modeling , learning in ACT-R

  18. Cuypers: user-centered hypermedia generator • CWI • Stefano Bocconi, AIO per 01-01-2002 • using knowledge on graphical design and communication in the application domain

  19. GO: Graphical Ontologies • IKAT/UM (Floris Wiesman) • ‘Generic tool for searching (navigating), accessing, and editing ontologies’ • MetaBrowser: a graphical browser for information retrieval

  20. Goal of the meeting • a lot of mono-disciplinary research exists • … based on toy problems or artificial data (TREC, multimedia retrieval benchmark dBs) • … barely looking at the user requirements • I2RP  we can do it better!

  21. System: application/experimentation Rendering Semantics Multimedia retrieval application User Modeling Speech/Language

  22. System: application/experimentation GO Cuypers dB Multimedia retrieval application UI Spreekbuis Optima/ACT-R

  23. Dependencies GO Cuypers Rendering Semantics dB User Modeling UI Speech/Language Optima/ACT-R Spreekbuis

  24. Agenda • Group introduction • Bilateral discussions • Integration • Concrete goals: define • Milestones • Experimentation-platform specification • Demonstrable output

  25. Agenda bilateral 20-min. discussions • Room C001 • UM + RuG • UL + RuG • UM + UL • Room C002 • UL + CWI • UM + CWI • CWI + RuG

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