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Relation discovery from web data for competency management.

Zhu, Jianhan; Gonçalves, Alexandre L.; Uren, Victoria S.; Motta, Enrico; Pacheco, Roberto; Eisenstadt, Marc; Song, Dawei


Jianhan Zhu

Alexandre L. Gonçalves

Victoria S. Uren

Enrico Motta

Roberto Pacheco

Marc Eisenstadt

Dawei Song


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.

Journal Article Type Article
Publication Date Dec 31, 2007
Journal Web intelligence and agent systems
Print ISSN 1570-1263
Publisher IOS Press
Peer Reviewed Peer Reviewed
Volume 5
Issue 4
Pages 405-417
Institution Citation ZHU, J., GONCALVES, A.L., UREN, V.S., MOTTA, E., PACHECO, R., EISENSTADT, M. and SONG, D. 2007. Relation discovery from web data for competency management. Web intelligence and agent systems [online], 5(4), pages 405-417. Available from: https://content.iospres...tional-journal/wia00124
Keywords Relation discovery; Clustering; Named entity recognition
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