1. Computer Science Department California Polytechnic State University San Luis Obispo, CA, U.S.A. Franz J. Kurfess Knowledge Retrieval

2. Computer Science Department California Polytechnic State University San Luis Obispo, CA, U.S.A. Franz J. Kurfess Knowledge Retrieval

3. Some of the material in these slides was developed for a lecture series sponsored by the European Community under the BPD program with Vilnius University as host institution Acknowledgements

4. 4 Use and Distribution of these Slides These slides are primarily intended for the students in classes I teach. In some cases, I only make PDF versions publicly available. If you would like to get a copy of the originals (Apple KeyNote or Microsoft PowerPoint), please contact me via email at fkurfess@calpoly.edu. I hereby grant permission to use them in educational settings. If you do so, it would be nice to send me an email about it. If you’re considering using them in a commercial environment, please contact me first.fkurfess@calpoly.edu

5. This lecture series has been sponsored by the European Community under the BPD program with Vilnius University as host institution Acknowledgements

6. 6 © Franz J. Kurfess Overview Exploratory Search  Limitations of Keywoard-based Search  Exploratory Search  Knowledge Retrieval Aspects  User Interaction Aspects  Collaborative Aspects  Faceted Search

7. 7 © Franz J. Kurfess Logistics

8. 8 Preliminaries

9. 9 © Franz J. Kurfess Bridge-In  What are examples of situations where keyword-based search is not suitable?

10. 10 Motivation and Objectives

11. 11 © Franz J. Kurfess Motivation

12. 12 © Franz J. Kurfess Objectives

13. 13 © Franz J. Kurfess Evaluation Criteria

14. 14 Exploratory Search Knowledge Retrieval Aspects User Interaction Aspects Collaborative Aspects

15. 15 © Franz J. Kurfess Exploratory Search ❖ finding knowledge through association ❖ user tries to find something without a priori knowledge  lack of keywords ❖ hypothesis: Human-made associations between knowledge items are valuable for others  especially if the associations are made by experts or experienced users

16. 16 © Franz J. Kurfess User Model Exploratory Search ❖ user submits tentative queries ❖ explores retrieved information to identify relevant or interesting items  actively seeks interesting information  is passively influenced by cues  provided by the searching mechanism or by retrieved entities

17. 17 © Franz J. Kurfess Activity: Modern Exploratory Search ❖ What are current concepts, methods and tools that enable exploratory search?

18. 18 © Franz J. Kurfess Vannevar Bush: Memex ❖ better knowledge management for scientific document collections  build, maintain, and share paths through the document space containing knowledge (“knowledge trails”)  see Vannevar Bush, “As We May Think”, Atlantic Monthly, July 1945; www. theatlantic.com/194507/bushwww. theatlantic.com/194507/bush

19. 19 © Franz J. Kurfess Exploratory Search for Knowledge Identification ❖ goal is to find out if knowledge about a particular topic or aspect exists or not  in contrast to finding additional information about a known topic

20. 20 © Franz J. Kurfess Exploratory Search Methods

21. 21 © Franz J. Kurfess Exploratory Browsing

22. 22 © Franz J. Kurfess Following Trails

23. 23 © Franz J. Kurfess Collaborative Filtering ❖ user communities work together to identify desirable or undesirable documents or entities  stars or hearts to express desirability  tags or labels  content-related  descriptive

24. 24 User Interaction for Exploration emphasis on interaction between user and computer for exploratory search interaction among users is discussed under “Collaboration”

25. 25 © Franz J. Kurfess User Models for Exploratory Search ❖ cues from other users serve as hints for the exploration ❖ often adaptations of keyword-based search models

26. 26 Faceted Search Intrinsic Properties Extrinsic Properties

27. 27 © Franz J. Kurfess Faceted Search ❖ exploration of a domain via attributes  select a relevant attribute, and display the elements of the domain ordered according to the attribute

28. 28 © Franz J. Kurfess Intrinsic Properties ❖ properties inseparable from an object or concept  shape, color, location,

29. 29 © Franz J. Kurfess Extrinsic Properties ❖ properties associated with objects or concepts by outside powers  this dog’s name is “Waldi”  vehicle license plate

30. 30 © Franz J. Kurfess Activity: Faceted Search Outside of Web Browsers ❖ What are tools or applications that employ faceted search to display items to the user?

31. 31 © Franz J. Kurfess Faceted Search in iTunes

32. 32 © Franz J. Kurfess Variations on Faceted Search ❖ displaying lists of items ordered according to an attribute can get quite boring ❖ attributes often lend themselves to alternative presentation methods  visual  static  color, size, shape  dynamic  movement, changes over time  auditory  often for supplementary information

33. 33 © Franz J. Kurfess Faceted Search in iTunes

34. 34

35. 35 Ausklang

36. 36 © Franz J. Kurfess Post-Test

37. 37 © Franz J. Kurfess Evaluation ❖ Criteria

38. 38 © Franz J. Kurfess KP/KM Activity ❖ select a domain that requires significant human involvement for dealing with knowledge ❖ identify at least two candidates for  knowledge representation  reasoning ❖ evaluate their suitability  human perspective  understandable and usable for humans  computational perspective  storage, processing

39. 39 © Franz J. Kurfess KP/KM Activity Outcomes 2007 ❖ Images with Metadata ❖ Extracting contact information from text ❖ Qualitative and quantitative knowledge about cheese making ❖ Visualization of astronomy data ❖ Surveillance/security KM ❖ Marketing ❖ Face recognition ❖ Visual marketing

40. 40 © Franz J. Kurfess Important Concepts and Terms ❖ automated reasoning ❖ belief network ❖ cognitive science ❖ computer science ❖ deduction ❖ frame ❖ human problem solving ❖ inference ❖ intelligence ❖ knowledge acquisition ❖ knowledge representation ❖ linguistics ❖ logic ❖ machine learning ❖ natural language ❖ ontology ❖ ontological commitment ❖ predicate logic ❖ probabilistic reasoning ❖ propositional logic ❖ psychology ❖ rational agent ❖ rationality ❖ reasoning ❖ rule-based system ❖ semantic network ❖ surrogate ❖ taxonomy ❖ Turing machine

41. 41 © Franz J. Kurfess Summary Exploratory Search ❖ keyword-based search is not always suitable  users are unfamiliar with the domain terminology  no clear goal for a search ❖ exploration of “knowledge spaces” can be done via  structure of the space  similarity between concepts or entities  overlap in user interests  serendipity ❖ faceted search is based on the use of properties of entities  sorting by one or more properties  identification through a combination of entities