Relation discovery from web data for competency management

Article type: Research Article

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

Affiliations: Knowledge Media Institute and Centre for Research in Computing, The Open University, Milton Keynes, United Kingdom | Stela Institute, Florianópolis, Brazil

Note: [] Corresponding author. E-mail: [email protected]

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.

Keywords: Relation discovery, clustering, named entity recognition

Journal: Web Intelligence and Agent Systems: An international journal, vol. 5, no. 4, pp. 405-417, 2007