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Search and Browsing Cycle for Knowledge Discovery and Learning
 

Search and Browsing Cycle for Knowledge Discovery and Learning

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

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

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    Search and Browsing Cycle for Knowledge Discovery and Learning Search and Browsing Cycle for Knowledge Discovery and Learning Presentation Transcript

    • Search and Browsing Cycle for Knowledge Discovery and Learning Sebastian Ryszard Kruk sebastian.kruk@deri.org DERI, NUI Galway 1
    • Search and Browsing Cycle for Knowledge Discovery and Learning Sebastian Ryszard Kruk sebastian.kruk@deri.org DERI, NUI Galway 1
    • Take away message 2 2
    • Take away message • We search in different ways for different things 2 2
    • Take away message • We search in different ways for different things • Keyword search is not enough 2 2
    • 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 2 2
    • 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 3 3
    • Motivation 4
    • Motivation How to discover and integrate knowledge coming from both formal and informal sources? 4
    • Motivation How to share and interconnect knowledge among people? 4
    • How do people search? • Different user goals: 5 5
    • 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.) 5 5
    • 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 5 5
    • 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 5 5
    • Search and browsing 6 6
    • Search and browsing • Why? • Knowledge can be useful • Not everything is a useful knowledge 6 6
    • Search and browsing • Why? • Knowledge can be useful • Not everything is a useful knowledge • How? (Search and browsing actions) 6 6
    • 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) 6 6
    • 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) 6 6
    • 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) 6 6
    • Keyword-based search Why is it not enough? 7 7
    • 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) 7 7
    • Keyword-based search How we can improve? 8 8
    • 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 8 8
    • Keyword-based search What’s next? 9 9
    • Keyword-based search What’s next? • “Tell me why” button and the transcript of refinement process • Continue to faceted navigation 9 9
    • Faceted navigation Why we need that? 10 10
    • 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 10 10
    • Faceted navigation How we do better? 11 11
    • 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 11 11
    • Browsing the data graph 12 12
    • Browsing the data graph MultiBeeBrowse exploits interconnected data ... 12 12
    • Browsing the data graph 13 13
    • Browsing the data graph ... to allow faceted navigation 13 13
    • Social Semantic Collaborative Filtering 14 14
    • Social Semantic Collaborative Filtering Why do we need collaboration? • The bottom-line of acquiring knowledge: informal communication (“word of mouth”) 14 14
    • Social Semantic Collaborative Filtering 15 15
    • 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) 15 15
    • Social Semantic Collaborative Filtering 16 16
    • 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) 16 16
    • Social Semantic Collaborative Filtering 17 17
    • 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.) 17 17
    • Social Semantic Collaborative Filtering 18 18
    • 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 18 18
    • Social Semantic Collaborative Filtering 19 19
    • Social Semantic Collaborative Filtering know s include bookmark 19 19
    • Social Semantic Collaborative Filtering know s include bookmark 19 19
    • Putting it all together 20 20
    • Putting it all together 20 20
    • Putting it all together user profile: user’s interests refine search results 20 filter, record, annotate, and share results and actions 20
    • 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 20 filter, record, annotate, and share results and actions 20
    • Search and Browsing in e-Learning space 21 21
    • Search and Browsing in e-Learning space 21 21
    • Search and Browsing in e-Learning space 21 21
    • Search and Browsing in e-Learning space 21 21
    • 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/ 21 21