Search and Browsing Cycle for Knowledge Discovery and Learning

Slide 1: Search and Browsing Cycle for Knowledge Discovery and Learning Sebastian Ryszard Kruk sebastian.kruk@deri.org DERI, NUI Galway 1

Slide 2: Search and Browsing Cycle for Knowledge Discovery and Learning Sebastian Ryszard Kruk sebastian.kruk@deri.org DERI, NUI Galway 1

Slide 3: Take away message 2 2

Slide 4: Take away message • We search in different ways for different things 2 2

Slide 5: Take away message • We search in different ways for different things • Keyword search is not enough 2 2

Slide 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 2 2

Slide 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 3 3

Slide 8: Motivation 4

Slide 9: Motivation How to discover and integrate knowledge coming from both formal and informal sources? 4

Slide 10: Motivation How to share and interconnect knowledge among people? 4

Slide 11: How do people search? • Different user goals: 5 5

Slide 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.) 5 5

Slide 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 5 5

Slide 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 5 5

Slide 15: Search and browsing 6 6

Slide 16: Search and browsing • Why? • Knowledge can be useful • Not everything is a useful knowledge 6 6

Slide 17: Search and browsing • Why? • Knowledge can be useful • Not everything is a useful knowledge • How? (Search and browsing actions) 6 6

Slide 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) 6 6

Slide 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) 6 6

Slide 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) 6 6

Slide 21: Keyword-based search Why is it not enough? 7 7

Slide 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) 7 7

Slide 23: Keyword-based search How we can improve? 8 8

Slide 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 8 8

Slide 25: Keyword-based search What’s next? 9 9

Slide 26: Keyword-based search What’s next? • “Tell me why” button and the transcript of refinement process • Continue to faceted navigation 9 9

Slide 27: Faceted navigation Why we need that? 10 10

Slide 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 10 10

Slide 29: Faceted navigation How we do better? 11 11

Slide 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 11 11

Slide 31: Browsing the data graph 12 12

Slide 32: Browsing the data graph MultiBeeBrowse exploits interconnected data ... 12 12

Slide 33: Browsing the data graph 13 13

Slide 34: Browsing the data graph ... to allow faceted navigation 13 13

Slide 35: Social Semantic Collaborative Filtering 14 14

Slide 36: Social Semantic Collaborative Filtering Why do we need collaboration? • The bottom-line of acquiring knowledge: informal communication (“word of mouth”) 14 14

Slide 37: Social Semantic Collaborative Filtering 15 15

Slide 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) 15 15

Slide 39: Social Semantic Collaborative Filtering 16 16

Slide 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) 16 16

Slide 41: Social Semantic Collaborative Filtering 17 17

Slide 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.) 17 17

Slide 43: Social Semantic Collaborative Filtering 18 18

Slide 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 18 18

Slide 45: Social Semantic Collaborative Filtering 19 19

Slide 46: Social Semantic Collaborative Filtering know s include bookmark 19 19

Slide 47: Social Semantic Collaborative Filtering know s include bookmark 19 19

Slide 48: Putting it all together 20 20

Slide 49: Putting it all together 20 20

Slide 50: Putting it all together user profile: user’s interests refine search results 20 filter, record, annotate, and share results and actions 20

Slide 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 20 filter, record, annotate, and share results and actions 20

Slide 52: Search and Browsing in e-Learning space 21 21

Slide 53: Search and Browsing in e-Learning space 21 21

Slide 54: Search and Browsing in e-Learning space 21 21

Slide 55: Search and Browsing in e-Learning space 21 21

Slide 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/ 21 21