Search and Browsing Cycle for Knowledge Discovery and Learning

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 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 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 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 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 know s include bookmark 19 19

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 Sebastian Ryszard Kruk eLearning Cluster DERI, NUI Galway sebastian.kruk@deri.org http://elite.deri.org/ http://www.corrib.org/ 21 21

Search and Browsing Cycle for Knowledge Discovery and Learning

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