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Colledge - A Vision of Collaborative Knowledge Networks

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Metzger,  Steffen
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Hose,  Katja
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Schenkel,  Ralf
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Citation

Metzger, S., Hose, K., & Schenkel, R. (2012). Colledge - A Vision of Collaborative Knowledge Networks. In 2nd International Workshop on Semantic Search over the Web. New York, NY: ACM. doi:10.1145/2494068.2494069.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0015-1CD7-A
Abstract
More and more semantic information has become available as RDF data recently, with the linked open data cloud as a prominent example. However, participating in the Semantic Web is cumbersome. Typically several steps are involved in using semantic knowledge. Information is first acquired, e.g. by information extraction, crowd sourcing or human experts. Then ontologies are published and distributed. Users may apply reasoning and otherwise modify their local ontology instances. However, currently these steps are treated separately and although each involves human effort, nearly no synergy effect is used and it is also mostly a one way process, e.g. user feedback hardly flows back into the main ontology version. Similarly, user cooperation is low. While there are approaches alleviating some of these limitations, e.g. extracting information at query time, personalizing queries, and integration of user feedback, this work combines all the pieces envisioning a social knowledge network that enables collaborative knowledge generation and exchange. Each aforementioned step is seen as a particular implementation of a network node responding to knowledge queries in its own way, e.g. by extracting it, applying reasoning or asking users, and learning from knowledge exchanged with neighbours. Original knowledge as well as user feedback is distributed over the network based on similar trust and provenance mechanisms. The extended query language we call for also allows for personalization.