Relation discovery from web data for competency management

Zhu, Jianhan, Gonçalves, Alexandre L., Uren, Victoria S., Motta, Enrico, Pacheco, Roberto, Song, Dawei and Eisenstadt, Marc (2007). Relation discovery from web data for competency management. Web Intelligence and Agent Systems, 5 (4), pp. 405-417.

Abstract

In current organizations, valuable enterprise knowledge is often buried under rapidly expanding huge amount of unstructured information in the form of web pages, blogs, and other forms of human text communications. We present a novel unsupervised machine learning method called CORDER (COmmunity Relation Discovery by named Entity Recognition) to turn these unstructured data into structured information for knowledge management in these organizations. CORDER exploits named entity recognition and co-occurrence data to associate individuals in an organization with their expertise and associates. We discuss the problems associated with evaluating unsupervised learners and report our initial evaluation experiments in an expert evaluation, a quantitative benchmarking, and an application of CORDER in a social networking tool called BuddyFinder.

Divisions: Aston Business School > Operations & information management
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Aston Business School > Operations & information management research group
Uncontrolled Keywords: relation discovery,named entity recognition,clustering
Full Text Link: http://oro.open ... journal-zhu.pdf
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Published Date: 2007
Authors: Zhu, Jianhan
Gonçalves, Alexandre L.
Uren, Victoria S.
Motta, Enrico
Pacheco, Roberto
Song, Dawei
Eisenstadt, Marc

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