Virtual Community Modelling to Support Knowledge Sharing

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Virtual Community Modelling to Support Knowledge
Sharing
School of Computing FACULTY OF ENGINEERING

• Kleanthous Styliani
• www.comp.leeds.ac.uk/stellak

Supervised by Dimitrova Vania
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Virtual Communities PhD Overview KSBP
Algorithms Results Future Work

• Research Focus
• Provide holistic personalised support in VC
• What is a Virtual Community?
• People using computers and the internet to
  communicate and share interests knowledge

Loosely Structured
Closely-Knit

• Educational
• Organisational

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Virtual Communities PhD Overview KSBP
Algorithms Results Future Work
Chracteristics of Closely Knit VC

• Controlled Membership
• Common Purpose
• Shared Interests

• Sharing information
• Generation of new Knowledge
• Collaboration

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Virtual Communities PhD Overview KSBP
Algorithms Results Future Work
Duplication of resources
Ignorance of others knowledge

New members not integrate
Fischer Ostwald (2001)
Active members becoming inactive
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Virtual Communities PhD Overview KSBP
Algorithms Results Future Work

• Our Approach
• Develop intelligent techniques to automatically
  detect required support tailored to the community
  
• User Modelling Approaches
• VC as an entity

What processes are important for the effective
functioning of a community and how can they be
supported?
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Virtual Communities PhD Overview KSBP
Algorithms Results Future Work

• Shared Mental Models
• Transactive Memory
• Cognitive Centrality

Convert Data
Paper at UM 2007 Conference
Ontology
Kleanthous, S. and Dimitrova, V. (2007) A
Semantic-Enhanced Approach for Modelling
Relationships and Centrality in Virtual
Communities, SociUM 1st Workshop on Adaptation
and Personalisation in Social Systems Groups,
Teams, Communities at 11th International
Conference on User Modeling, Corfu, Greece 
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Virtual Communities PhD Overview KSBP
Algorithms Results Future Work
Community Model Application
Relationship model extracted as graphs ReadSim,
UploadSim and InterestSim Undirected
graphs ReadRes Directed graph Problems
Identified
Paper at AH 2008 Conference
Kleanthous, S. and Dimitrova, V. (2008) Modelling
Semantic Relationships and Centrality to
Facilitate Community Knowledge Sharing, 5th
International Conference on Adaptive Hypermedia
Adaptive Web-Based Systems (AH'08), Hannover,
Germany
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Virtual Communities PhD Overview KSBP
Algorithms Results Future Work
Automatic graph-based pattern detection
P1 Two members have a ltrelationshipgt with the
same members but not among themselves

• Importance
• Improve TM System
• Promote Collaboration

ReadSim for members a and b
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Virtual Communities PhD Overview KSBP
Algorithms Results Future Work
P2 A member who appears uploading but not
downloading and others are reading what he/she is
uploading.

• Importance
• Motivate to start downloading
• Make member aware of similar members
• Develop SMM
• Collaboration

For member a
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Virtual Communities PhD Overview KSBP
Algorithms Results Future Work
P3 A member appears to download only and has
InterestSim with other members.

• Importance
• Motivate uploading
• Development of SMM
• Improve TM system
• Collaboration

For member a

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Virtual Communities PhD Overview KSBP
Algorithms Results Future Work
Algorithm application Results
Study from October 2005 Dec 2006
Results from study October 2005 May 2006

• Barriers for
• Development of TM system
• Building of SMM
• Collaboration not possible

Results from study January, February and March
2006

• Each month closer to stop functioning
• More problems, identified on March 2006 than on
  January 2006

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Virtual Communities PhD Overview KSBP
Algorithms Results Future Work
January 2006 Members 9 and 24 had ReadSim
relation with the same members as member 5 but
not with member 5. February 2006 Members 9 and
24 disengage from the community

• Support
• Automatic messages to members 9 and 24 to make
  them aware of their similarity to 5 and others.
• development of a better TM system
• open the doors for collaboration

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Virtual Communities PhD Overview KSBP
Algorithms Results Future Work
January 2006 member 28 appears to be a
connecting node between members 5, 9, 24 and 31.
All four members appear to have ReadSim with
member 28 but not among themselves.

• Support
• Make 28 aware of his importance to the community
• Encourage 28 to collaborate with members 5, 9, 24
  and 31
• Make 5, 9, 24 and 31 aware of their connections
  through 28
• Facilitate SMM
• Improve TM System
• Support Knowledge Sharing

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Virtual Communities PhD Overview KSBP
Algorithms Results Future Work
January 2006 one member was only uploading and
four members were only downloading. March 2006
two members were only uploading and nine members
only downloading. e.g. Member 19 downloaded
thirty-three resources, without uploading
anything from January to March

• Support
• provide information of members with similar
  interests based on InterestSim
• members who are reading similar resources
  (ReadSim)
• uploading resources similar to members
  interests (UploadSim).
• Motivate members to contribute
• Improve SMM, TM
• Facilitate knowledge sharing

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Virtual Communities PhD Overview KSBP
Algorithms Results Future Work
Future Work

• Algorithms for detecting community changes
  implemented applied to VC
• Analysis of results (currently working)
• Final Evaluation Study applying the whole
  framework
• Derive a community model
• Detect problematic situations
• Intervene to improve community functioning
• Analyse community changes after adaptive
  intervention
• Analyse subjective data

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Virtual Communities PhD Overview KSBP
Algorithms Results Future Work

• Summary
• Problem There is a need to support VC
• Solution Intelligent techniques tailored to the
  whole community can provide the foundations for a
  sustainable VC
• Results TM, SMM, CCen can be used to support VC
• Modelling semantic-enhanced relationships can
  help us to identify what support is needed
• Automatic pattern detection can help in
  generating adaptive interventions
• Future Work
• How changes in the VC over time can influence
  knowledge sharing?
• Evaluation of the approach on a different VC

Nov 5th 2008