Relation discovery from web data for competency management.

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

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.

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.iospress.com/articles/web-intelligence-and-agent-systems-an-international-journal/wia00124

• Journal Article Type: Article
• Acceptance Date: Dec 31, 2007
• Online Publication Date: Dec 31, 2007
• Publication Date: Dec 31, 2007
• Deposit Date: Mar 13, 2009
• Publicly Available Date: Mar 13, 2009
• Journal: Web intelligence and agent systems
• Print ISSN: 1570-1263
• Publisher: IOS Press
• Peer Reviewed: Peer Reviewed
• Volume: 5
• Issue: 4
• Pages: 405-417
• Keywords: Relation discovery; Clustering; Named entity recognition
• Public URL: http://hdl.handle.net/10059/317
• Publisher URL: https://content.iospress.com/articles/web-intelligence-and-agent-systems-an-international-journal/wia00124
• Contract Date: Mar 13, 2009