1. Systems and Users in Intelligent Information Retrieval: Who does What? prof. dr. L. Schomaker I 2 RP Symposium 3/2/2003, Delft

2. I 2 RP 2 Overview  Who?  Intelligent Information Retrieval and Presentation ( I 2 RP): The Challenge  The future I 2 RP

3. 3 Persons & Institutes  Supervisors: –prof. Lynda Hardman (CWI) –prof. Jaap van den Herik (UM) –prof. Gerard Kempen/Crit Cremers (UL) –dr. N. Taatgen (RuG)  Coordinator –prof. Lambert Schomaker (RuG) I 2 RP

4. 4 Persons & Institutes (continued…)  Researchers: –Stefano Bocconi (oio,CWI) –Yulia Bachvarova (oio,CWI) –Boban Arsenijevic (oio,UL) –Floris Wiesman (postdoc, UM) –Judith Grob (oio,RuG) I 2 RP

5. 5 Intelligent Information Retrieval and Presentation ( I 2 RP): The Challenge  Observations:  CPU power is ever increasing, but…  “Current systems in Information Retrieval are violating the essential rules for an intelligent dialogue” I 2 RP

10. 10 A mutually cooperative dialogue?  Grice (1975): the rules for a mutually cooperative dialogue are:

11. I 2 RP 11 Grice (1975)  Maxims of quantity: –Make your contribution as informative as required –Do not make your contribution more informative than required

12. I 2 RP 12 Grice (1975)  Maxims of quality: –Do not say what you believe to be false –Do not say that for which you lack evidence

13. I 2 RP 13 Maxims of Grice (1975)  Maxim of relation: –Be relevant

14. I 2 RP 14 Maxims of Grice (1975)  Maxim of manner: –Avoid obscure expressions –Avoid ambiguity –Be orderly –Be brief

15. I 2 RP 15 Example: Quantity  “when did Napoleon die?”

16. I 2 RP 16 Example: Quantity  “when did Napoleon die?”  72800 documents found

17. I 2 RP 17 How to design systems that obey the Maxims of Grice?  Use Knowledge!  Use the User!  Use language!  Starting point: The “user in context”

18. I 2 RP 18 (1) Knowledge  Use Knowledge!  What Knowledge?  Who specifies it?  How to relate knowledge from heterogeneous data bases?

19. I 2 RP 19 (2) The User  Use the User!  Will they be motivated?  What type of user? Skilled / newbie?  What does the user WANT?  Can we predict user actions?  How to reason like the current user?

20. I 2 RP 20 Example: Relevance Feedback in Image Search  Machine Learning may give us a free ride on Moore’s Law ( f cpu increases each year)

21. I 2 RP 21 Example: Relevance Feedback in Image Search  Machine Learning may give us a free ride on Moore’s Law ( f cpu increases each year)  But: Pattern classification needs examples (ground truth values) given by users

22. I 2 RP 22 Example: Relevance Feedback in Image Search  Machine Learning may give us a free ride on Moore’s Law ( f cpu increases each year)  But: Pattern classification needs examples (ground truth values) given by users  In Information Retrieval, this is implemented as “relevance feedback”, given by the user, on quality of items in a hit list

23. Relevance Feedback in Image Search …

24. Relevance Feedback in Image Search

25. users are lazy, especially if the perceived benefits are low…

26. The machine may find structure… (Kohonen self- organized map of scanned handwritten characters)

27. The machine may find structure… But human ground truth labels are still necessary!

28. I 2 RP 28 … the user …  Knowledge on user skill development is essential. What is annoying at start may be easy later (and vice versa).  What is the user’s goal? How do users maintain their “goal stack”?

29. I 2 RP 29 (3) Language  Use language!  Can the system parse input sentences?  Can the system generate text answers from non-linguistic data and knowledge bases?  How to generate a narrative from a sequence of facts?

30. I 2 RP 30 Project: Floris Wiesman (UM) “Instance vs Term-based Ontology Mappings”  Given two ontologies from the cultural heritage, how can the knowledge be shared?  (manual translation? X)  Try to find correspondences in naming schemes, as if the mapping problem were an Information Retrieval problem in itself.

31. I 2 RP 31 Ontology Mapping (Wiesman)  Try to find correspondences in naming schemes, as if the mapping problem were an Information Retrieval problem in itself.  Example: Ontology “museum-A”: Document->Book->Author->Name Ontology “library-B”: Document->Book->Writer->Name

32. I 2 RP 32 Ontology Mapping (Wiesman  POSTER)  Try to find correspondences in naming schemes, as if the mapping problem were an Information Retrieval problem in itself.  Example: Ontology “museum-A”: Document->Book->Author->Name Ontology “library-B”: Document->Book->Writer->Name

33. I 2 RP 33 Projects: Stefano Bocconi (CWI)  How to develop discourse models: system response  user’s question –Narrative(“Tell me about…”) –Description(“What is …?”) –Explanation(“Why is…?”) –Argument (“Why should …?”)  Experimental environment:  “Rembrandt’s World” Ontology

34. I 2 RP 34 Projects: Stefano Bocconi (CWI)  How to develop discourse models: system response  user’s question –Narrative(“Tell me about…”) –Description(“What is …?”) –Explanation(“Why is…?”) –Argument (“Why should …?”)  Experimental environment:  “Rembrandt’s World” Ontology POSTER

35. I 2 RP 35 Projects: Boban Arsenijevic (UL)  How to Parse & Generate using an intermediate semantic processing stage?  Input sentence  parsing  “Aggregate Semantic Material  generator  Output sentences  Goal: explore how different phrasings still pertain to the semantic core

36. I 2 RP 36 Projects: Boban Arsenijevic (UL)  How to Parse & Generate using an intermediate semantic processing stage?  Input sentence  parsing  “Aggregate Semantic Material  generator  Output sentences  Goal: explore how different phrasings still pertain to the semantic core POSTER + demo: parser/generator for Dutch

37. I 2 RP 37 Projects: Judith Grob (RuG)  How to develop an active user agent that learns from the user and behaves in a way which is acceptable and useful?  Cognitive modeling (ACT-R), skill development and concept learning by humans  “Instance-based” learning schemes are a method to find analogies between patterns

38. I 2 RP 38 Projects: Judith Grob (RuG)  (just started). An initial model concerns the modeling of learning a simple task with a quantitative target variable (“Sugar Factory”)  ACT-R appears to be able to mimic human learning and ‘transfer’  A similar goal-oriented task in information retrieval will be developed

39. I 2 RP 39 Projects: Judith Grob (RuG)  (just started). An initial model concerns the modeling of learning a simple task with a quantitative target variable (“Sugar Factory”)  ACT-R appears to be able to mimic human learning and ‘transfer’  A similar goal-oriented task in information retrieval will be developed POSTER

40. I 2 RP 40 Future developments  Partial overlap between projects is noted and exploited: –“Rembrandt’s World” is a useful example ontology –Goal: interoperability over the network –First: develop bilateral cooperation between partners  cooperation yields co-publication and software combination

41. I 2 RP 41 Conclusion  I 2 RP represents a multi-faceted view on system and user in an information-retrieval context  Multi-disciplinarity: CS,AI,Cognition,Language  Still: a common ground starts to develop!