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

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

Authors

Jianhan Zhu

Alexandre L. Gon�alves

Victoria S. Uren

Enrico Motta

Roberto Pacheco

Marc Eisenstadt

Dawei Song

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