Abstract
Some of today’s most widely spread applications are social systems where people can form communities and share knowledge. However, knowledge sharing is not always effective and communities often do not sustain. Can user modelling approaches help to identify what support could be offered and how this would benefit the community? The paper presents algorithms for extracting a model of a closely-knit virtual community following processes identified as important for effective communities. The algorithms are applied to get an insight of a real virtual community and to identify what support may be needed to help the community function better as an entity.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bretzke, H., Vassileva, J.: Motivating Cooperation on Peer to Peer Networks. In: 9th Int. Conf. on User Modelling, USA (2003)
Cheng, R., Vassileva, J.: Design and evaluation of an adaptive incentive mechanism for sustained educational online communities. UMUAI V16(3), 348 (2006)
Falkowski, T., Spiliopoulou, M.: Users in Volatile Communities: Studying Active Participation and Community Evolution. In: User Modeling, pp. 47–56 (2007)
Fischer, G., Ostwald, J.: Knowledge Management: Problems, Promises, Realities, and Challenges. IEEE Intelligent Systems 16(1), 60–72 (2001)
Freeman, L.: Centrality in social networks conceptual clarification. Social Networks 1(3), 239 (1979)
Harper, M., et al.: Talk amongst yourselves: inviting users to participate in online conversations. In: IUI 2007. ACM Press, New York (2007)
Ilgen, D.R., et al.: Teams in Organizations: From Input - Process - Output Models to IMOI Models. Annual Review of Psychology, 517–543 (2005)
Kay, J., et al.: The Big Five and Visualisations of Team Work Activity. In: Intelligent Tutoring Systems, pp. 197–206 (2006)
Kleanthous, S.: Semantic-Enhanced Personalised Support for Knowledge Sharing in Virtual Communities. In: Conati, C., McCoy, K., Paliouras, G. (eds.) UM 2007. LNCS (LNAI), vol. 4511, pp. 465–469. Springer, Heidelberg (2007)
Kleanthous, S., Dimitrova, V.: Semantic-Enhanced Approach for Modelling Cognitive Relationships in Virtual Communities. In: SociUM workshop at UM 2007 (2007)
Lave, J., Wenger, E.: Situated Learning: Legitimate Peripheral Participation. Cambridge University Press, New York (1991)
Seco, N., Veale, T., Hayes, J.: An Intrinsic Information Content Metric for Semantic Similarity in WordNet. In: ECAI 2004, pp. 1089–1090 (2004)
Song, X., et al.: ExpertiseNet: Relational and Evolutionary Expert Modeling. LNCS, p. 108. Springer, Heidelberg (2005)
Wang, Y., Graether, W., Prinz, W.: Suitable notification intensity: the dynamic awareness system. In: Proc. of the int. ACM conf. on Supporting Group Work (2007)
Wegner, D.M.: Transactive Memory: A Contemporary Analysis of the Group Mind. In: Mullen, B., Goethals, G.R. (eds.) Theories of Group Behavior, pp. 185–208. Springer, Heidelberg (1986)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kleanthous, S., Dimitrova, V. (2008). Modelling Semantic Relationships and Centrality to Facilitate Community Knowledge Sharing. In: Nejdl, W., Kay, J., Pu, P., Herder, E. (eds) Adaptive Hypermedia and Adaptive Web-Based Systems. AH 2008. Lecture Notes in Computer Science, vol 5149. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70987-9_15
Download citation
DOI: https://doi.org/10.1007/978-3-540-70987-9_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70984-8
Online ISBN: 978-3-540-70987-9
eBook Packages: Computer ScienceComputer Science (R0)