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Semantic - Enhanced Community Modelling to Support Knowledge Sharing

School of Computing FACULTY OF ENGINEERING. Semantic - Enhanced Community Modelling to Support Knowledge Sharing. Kleanthous Styliani www.comp.leeds.ac.uk/stellak. School of Computing FACULTY OF ENGINEERING. Overview. This Research Algorithms Study Initial Results Community

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Semantic - Enhanced Community Modelling to Support Knowledge Sharing

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  1. School of Computing FACULTY OF ENGINEERING Semantic - Enhanced Community Modelling toSupport Knowledge Sharing Kleanthous Styliani www.comp.leeds.ac.uk/stellak

  2. School of Computing FACULTY OF ENGINEERING • Overview • This Research • Algorithms • Study • Initial Results • Community • Relationship Model • Centrality • Individual User Model Oct 30th 2007 Reading Group Session

  3. This Research Algorithms Study Initial Results • This Research… Research Focus: Provide holistic personalised support in VC • Main Assumptions: • Providing adaptation tailored to the community as a whole will help the community function better. • By promoting the building of TM, development of SMM, and establishment of CCs and identifying CCen inside the community, will improve the functioning of this community Oct 30th 2007 Reading Group Session

  4. This Research Algorithms Study Initial Results • This Research… Research Questions R1:How to extract a computational model to represent the functioning and evolution of the community as a whole, using semantically enhanced tracking data? R2:Using that model, how to provide personalised functionality to support the development of TM, building of SMM, establishment of CCs and identification of CCen? R3:How can personalised support of the above processes affect the functioning of the community? Oct 30th 2007 Reading Group Session

  5. This Research Algorithms Study Initial Results Community Model Community Model Application Community Model Acquisition CCs TM CCen SMM Oct 30th 2007 Reading Group Session

  6. This Research Algorithms Study Initial Results The Example Community - BSCW Oct 30th 2007 Reading Group Session

  7. This Research Algorithms Study Initial Results • Input Formalisation… ENVIRONMENT E: <HF, F, R, M, D> HF : Taxonomy of Folders Folder F: <FTitle, FCreator, FDescription, FDate> Resource R: <RCreatedData, RMetadata> RCreatedData: <RFolder, RName, RDescription, RRating, RCreator, RDate, RAssessor, RReader> Based on Dublin Core Metadata element set RMetadata: <RTitle, RAuthor, RSource, RKeywords, RDatePublish> Member M: <MName, MEmail, MDateJoin> Oct 30th 2007 Reading Group Session

  8. This Research Algorithms Study Initial Results • Community Model ReadDisc ReadRes Participation User Interests UploadSim Relationships Model Cognitive Centrality Individual User Models InterestSim ReadSim Relationships Personal Hierarchies Cognitively Central Members Community Context Popular Topics Peripheral Topics Oct 30th 2007 Reading Group Session

  9. This Research Algorithms Study Initial Results Modelling Relationships… WordNet ReadRes Relationship because A read resources uploaded by B Oct 30th 2007 Reading Group Session

  10. This Research Algorithms Study Initial Results Modelling Relationships… WordNet ReadSim & UploadSim ReadSim: Relationship because A reads resources similar to those B reads. UploadSim: Relationship because A uploads resources similar to those B uploads. Oct 30th 2007 Reading Group Session

  11. This Research Algorithms Study Initial Results Modelling Relationships… WordNet InterestSim Similarity between two members’ interests Oct 30th 2007 Reading Group Session

  12. This Research Algorithms Study Initial Results Capturing Centrality… Oct 30th 2007 Reading Group Session

  13. This Research Algorithms Study Initial Results The Study… • Run from Oct 2005 – Dec 2006 • BSCW data anonymised & converted into .txt • Extracted data using Java • Data stored on a MySQL Database • Input to algorithms to extract the Community Model Oct 30th 2007 Reading Group Session

  14. Initial Results • Community • Relationship Model • Centrality • Individual User Model Overview… Oct 30th 2007 Reading Group Session

  15. Initial Results • Community • Relationship Model • Centrality • Individual User Model Activity… Oct 30th 2007 Reading Group Session

  16. Initial Results • Community • Relationship Model • Centrality • Individual User Model Uploading… Oct 30th 2007 Reading Group Session

  17. Initial Results • Community • Relationship Model • Centrality • Individual User Model Downloading… Oct 30th 2007 Reading Group Session

  18. Initial Results • Community • Relationship Model • Centrality • Individual User Model ReadRes… • Support: • Identify complementary knowledge • Who holds information I am interested in? • Improve TM Oct 30th 2007 Reading Group Session

  19. Initial Results • Community • Relationship Model • Centrality • Individual User Model Reading Only… • Have ReadRes with the same members • Support: • Identify people who are interested in what I am interested. • Encourage Collaboration • -Building SMM • -Improve TM Oct 30th 2007 Reading Group Session

  20. Initial Results • Community • Relationship Model • Centrality • Individual User Model Member 2 Member 5 Member 9 • Only downloading • Have exactly the same ReadRes relations • Support: • Encourage collaboration • Motivate contribution Oct 30th 2007 Reading Group Session

  21. Initial Results • Community • Relationship Model • Centrality • Individual User Model ReadSim… • Support: • Identify relationships that members are not aware of • Who is reading resources similar to those I am reading? • Who is interested in similar resources as I am? • Improve TM Oct 30th 2007 Reading Group Session

  22. Initial Results • Community • Relationship Model • Centrality • Individual User Model • Reading resources from the same people • Support: • Develop awareness of this similarity • Improve TM/SMM • Encourage collaboration • Facilitate knowledge Sharing Oct 30th 2007 Reading Group Session

  23. Initial Results • Community • Relationship Model • Centrality • Individual User Model UploadSim… • Very strongly connected • Support: • Identify people who are not uploading & encourage them to contribute • Make people aware of their similarities • Improve SMM/TM • Support Collaboration Oct 30th 2007 Reading Group Session

  24. Initial Results • Community • Relationship Model • Centrality • Individual User Model InterestSim… • Support: • Identify interest similarity & complementarities • Who has interests similar to a given member? • Motivate contribution • Encourage collaboration • Improve SMM/TM Oct 30th 2007 Reading Group Session

  25. Initial Results • Community • Relationship Model • Centrality • Individual User Model Cognitive Centrality… Support: Where important knowledge is located? Where unique knowledge is located? Improves TM/SMM Motivation mechanism Oct 30th 2007 Reading Group Session

  26. Initial Results • Community • Relationship Model • Centrality • Individual User Model • Member 12 uploaded only one resource • 29.4% of the community read his resource • Support: • Display similar members, motivate to contribute/ read • Use ReadSim to motivate • Improve TM/SMM ReadRes Ego Network UploadSim Ego Network Oct 30th 2007 Reading Group Session

  27. Initial Results • Community • Relationship Model • Centrality • Individual User Model Newcomer Integration… ReadRes Ego Network of Member 19 • Support: • Use ReadRes to help member integrate. • Who holds knowledge important to this member? • Improve TM Oct 30th 2007 Reading Group Session

  28. Initial Results • Community • Relationship Model • Centrality • Individual User Model Integration Problem… • Member 33 uploaded 11 resources • Never read a resource • Support: • Help members like 33 to integrate • Identify similar members & motivate this member to contribute • Improve TM/SMM • Encourage Collaboration • Support Newcomer Integration UploadSim & InterestSim Ego Network Ego Network Oct 30th 2007 Reading Group Session

  29. This Research Algorithms Study Initial Results • Future Work… • Ontology integration • What will it be different? • Community model evaluation • Model community changes over time • Relationships • Individual • Extend an existing system • Evaluation with users Oct 30th 2007 Reading Group Session

  30. This Research Algorithms Study Initial Results • Summary… • TM, SMM, CCen can be used to support Virtual Communities • Modelling semantic-enhanced relationships can help us to identify what support is needed • A holistic support may provide the foundations for a sustainable virtual community Oct 30th 2007 Reading Group Session

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