Relation discovery from web data for competency management.
Zhu, Jianhan; Gonçalves, Alexandre L.; Uren, Victoria S.; Motta, Enrico; Pacheco, Roberto; Eisenstadt, Marc; Song, Dawei
Alexandre L. Gonçalves
Victoria S. Uren
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|
|Peer Reviewed||Peer Reviewed|
|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|
ZHU 2007 Relation discovery from web
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