COMBEK in Mexico: Community-centric Knowledge Evolution at OnTheMove 2008

De Leenheer, P., Hepp, M., and Sheth, A. (2008) COMBEK 2008 PC Co-chairs’ Message. OTM Workshops 2008, LNCS 5333, Springer, pp. 181-182

This year we held the first edition of the International Workshop on Community-centric evolution of knowledge-intensive systems (COMBEK). It was co-located with OnTheMove 2008 in Monterrey, Mexico.

dscn0837COMBEK seeks to address the need for research that explores and embraces the novel, challenging but crucial issue of adapting knowledge resources to their user communities, and use them as key instrument in building knowledge-intensive internet systems. Through a deep understanding of the real-time, community-driven, evolution of shared knowledge structures and knowledge bases including ontologies, folksonomies, and/or shared conceptual models, a Web-based knowledge-intensive system can be made operationally relevant and sustainable over long periods of time.

COMBEK’s primary value proposition is to accentuate the role of community.  The expected outcome is to innovate the science of ontology engineering and to unlock the expected (and unavoidable) paradigm shift in knowledge-based and community-driven systems. Such a paradigm would affect knowledge sharing and communication across diverse communities in business, industry, and society. We are further convinced that being a part of the On The Move Federated Conferences will turn a spotlight on the scientific issues addressed in COMBEK, making them visible and attractive to practioners and industry.

COMBEK advocates transcending the current, narrow “ontology engineering” view on change management of knowledge structures that is at the heart of today’s knowledge-intensive systems. We will consider stakeholder communities as integral factors in the continuous evolution of the knowledge-intensive systems in which they collaborate. By bringing together researchers from different domains, COMBEK aims to advance research on a very broad spectrum of needs, opportunities, and solutions.

COMBEK offered a forum for practitioners and researchers to meet, and exchange research and implementation ideas and results towards next-generation knowledge-intensive systems and radically new approaches in knowledge evolution. It gave practitioners and researchers an opportunity to present their work and to take part in open and extended discussions. Relevant topics include (but are not limited to) theoretical or empirical exploration and position papers on theory and method, as well as tool demonstrations, realistic case studies, and experience reports.dscn0851

Out of twenty-one full paper submissions, we selected six for inclusion in the official workshop proceedings, which results in an acceptance rate of less than 30%. We would like to thank all authors for their submissions and the members of the Program Committee for their time and rigor in reviewing the papers. The six papers address an interesting range of highly relevant research issues in this new field and will contribute to a better understanding of how the vision of community-based evolution of knowledge-intensive systems can be made a reality.

Van de Maele and Diaz introduce a two-phase approach to mapping heterogeneous semantic models, splitting up the traditional one-step process into a mapping phase that takes into account the actual community and context dimension, and a commitment phase. The proposal favors reuse, evolution, and scalability. Kotis motivates the needs for adopting semantic wiki technology for the delicate task argumentation and negotiation in collaborative ontology evolution. Debruyne et al. report on their work extending ontology negotiation with algorithms that afford (i) a statistical summary of how community stakeholders modify and extend their shared ontology base; and (ii) a negotiation protocol for incorporating consensus changes to iterate the ontology.

Baatarjav et al. present a statistical technique for predicting the community an individual would belong to, based on Facebook social network data. This work leads to interesting insights for group recommendations in social network systems. Along this line, Phithakkitnukoon and Dantu apply machine learning techniques on call log data from the MIT Media Lab’s reality mining corpus to classify mobile phone users into two social groups. The accuracy is enriched by feature selection based on a normalised mutual information heuristic. Finally, Van Damme and Christiaens introduce and demonstrate a novel approach to assess the semantic quality of tagging behaviour in narrow folksonomies. In particular, they propose lexical ambiguity to feature the measure quality of tags.

This first workshop has helped us to establish a respective research community; at the same time, we are fully aware of the fact that most of the research work is still to be accomplished. We are looking forward to working on these challenges with all colleagues in this emerging research community.

Brussels, Munich, Dayton

Pieter De Leenheer
Martin Hepp
Amit Sheth


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