Slides from the presentation I gave at 5th Annual Conference on Teaching & Learning: Learning Technologies, June 7/8, 2007

1. Search and Browsing Cycle
for Knowledge
Discovery and Learning
Sebastian Ryszard Kruk
sebastian.kruk@deri.org
DERI, NUI Galway
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2. Search and Browsing Cycle
for Knowledge
Discovery and Learning
Sebastian Ryszard Kruk
sebastian.kruk@deri.org
DERI, NUI Galway
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3. Take away message
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4. Take away message
• We search in different ways for different things
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5. Take away message
• We search in different ways for different things
• Keyword search is not enough
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6. Take away message
• We search in different ways for different things
• Keyword search is not enough
• We create the knowledge by sharing our (search) experience
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7. Outline
• Motivation
• How do people search
• Search and Browsing life-cycle
• Applying semantics and making use of social networks:
• Keyword-based search
• Faceted Navigation
• Collaborative Filtering
• Conclusions
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8. Motivation
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9. Motivation
How to discover and integrate knowledge
coming from both formal and informal sources?
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10. Motivation
How to share and interconnect knowledge among people?
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11. How do people search?
• Different user goals:
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12. How do people search?
• Different user goals:
– Resource Seeking - the user wants to find a specific
resource (e.g. lyrics of a song, a program to download, a
map service etc.)
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13. How do people search?
• Different user goals:
– Resource Seeking - the user wants to find a specific
resource (e.g. lyrics of a song, a program to download, a
map service etc.)
– Navigational - the user is searching for a specific web site
whose URL s/he forgot
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14. How do people search?
• Different user goals:
– Resource Seeking - the user wants to find a specific
resource (e.g. lyrics of a song, a program to download, a
map service etc.)
– Navigational - the user is searching for a specific web site
whose URL s/he forgot
– Informational - the user is looking for information about a
topic s/he is interested in
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15. Search and browsing
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16. Search and browsing
• Why?
• Knowledge can be useful
• Not everything is a useful knowledge
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17. Search and browsing
• Why?
• Knowledge can be useful
• Not everything is a useful knowledge
• How? (Search and browsing actions)
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18. Search and browsing
• Why?
• Knowledge can be useful
• Not everything is a useful knowledge
• How? (Search and browsing actions)
– [REUSE] keyword-based search (resource seeking)
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19. Search and browsing
• Why?
• Knowledge can be useful
• Not everything is a useful knowledge
• How? (Search and browsing actions)
– [REUSE] keyword-based search (resource seeking)
– [REDUCE] faceted navigation (navigational)
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20. Search and browsing
• Why?
• Knowledge can be useful
• Not everything is a useful knowledge
• How? (Search and browsing actions)
– [REUSE] keyword-based search (resource seeking)
– [REDUCE] faceted navigation (navigational)
– [RECYCLE] collaborative filtering (informational)
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21. Keyword-based search
Why is it not enough?
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22. Keyword-based search
Why is it not enough?
• Too many results (low precision)
• One needs to specify the exact keyword (low recall)
• How to distinguish between: Python and python? (high
fall-out)
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23. Keyword-based search
How we can improve?
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24. Keyword-based search
How we can improve?
• Disambiguation through a context
• Long-term: user’s interests, engine type
• Short-term: user’s goal, location, time
• Query
• Query refinement
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25. Keyword-based search
What’s next?
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26. Keyword-based search
What’s next?
• “Tell me why” button and the transcript of refinement
process
• Continue to faceted navigation
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27. Faceted navigation
Why we need that?
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28. Faceted navigation
Why we need that?
• The search does not end on a (long) list of results
• The results are not a list (!) but a graph
• „Lost in hyper-space”
• A need for unified UI and services for filter/narrow and
browse/expand services
• Share browsing experience – navigate
collaboratively
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29. Faceted navigation
How we do better?
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30. Faceted navigation
How we do better?
• A set of navigation services: access, search, filter,
similar, browse, and combine
• Auxiliary services: meta, context, and statistics
• Zoom-able, adaptable, and accessible user interface
• Engage users in collaborative browsing
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31. Browsing the data graph
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32. Browsing the data graph
MultiBeeBrowse exploits interconnected data ...
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33. Browsing the data graph
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34. Browsing the data graph
... to allow faceted navigation
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35. Social Semantic
Collaborative Filtering
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36. Social Semantic
Collaborative Filtering
Why do we need collaboration?
• The bottom-line of acquiring knowledge: informal
communication (“word of mouth”)
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37. Social Semantic
Collaborative Filtering
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38. Social Semantic
Collaborative Filtering
How can that help?
• Everyone classifies (filters) the information in bookmark
folders (user-oriented taxonomy)
• Peers share (collaborate over) the information (community-
driven taxonomy)
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39. Social Semantic
Collaborative Filtering
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40. Social Semantic
Collaborative Filtering
What do we got?
• Knowledge “flows“ from the expert through the social
network to the user
• Systems amass a lot of information on user/community
profile (context)
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41. Social Semantic
Collaborative Filtering
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42. Social Semantic
Collaborative Filtering
What problems can we
encounter?
• The horizon of a social network (2-3 degrees of separation)
• How to handle fine-grained information
(blogs, wikis, etc.)
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43. Social Semantic
Collaborative Filtering
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44. Social Semantic
Collaborative Filtering
How to solve them?
• Inference engine to suggest knowledge from the outskirts
of the social network
• Support for Semantically Interlinked Online Communities
(SIOC) metadata
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45. Social Semantic
Collaborative Filtering
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46. Social Semantic
Collaborative Filtering
know
s
include
bookmark
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47. Social Semantic
Collaborative Filtering
know
s
include
bookmark
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48. Putting it all together
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49. Putting it all together
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50. Putting it all together
user profile:
user’s interests
refine
search results
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filter, record, annotate, and share results and actions
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51. Putting it all together
user profile:
user’s interests
user profile:
recent actions
refine
filter, record,
search results
annotate,
and share results
re-call shared actions
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filter, record, annotate, and share results and actions
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52. Search and Browsing
in e-Learning space
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53. Search and Browsing
in e-Learning space
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54. Search and Browsing
in e-Learning space
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55. Search and Browsing
in e-Learning space
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56. Search and Browsing
in e-Learning space
Sebastian Ryszard Kruk
eLearning Cluster
DERI, NUI Galway
sebastian.kruk@deri.org
http://elite.deri.org/
http://www.corrib.org/
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