Relation discovery from web data for competency management

  • Authors:

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

  • Affiliations:

  • (Correspd. E-mail: j.zhu@open.ac.uk) Knowledge Media Institute and Centre for Research in Computing, The Open University, Milton Keynes, United Kingdom;Stela Institute, Florianópolis, Brazil;Knowledge Media Institute and Centre for Research in Computing, The Open University, Milton Keynes, United Kingdom;Knowledge Media Institute and Centre for Research in Computing, The Open University, Milton Keynes, United Kingdom;Stela Institute, Florianópolis, Brazil;Knowledge Media Institute and Centre for Research in Computing, The Open University, Milton Keynes, United Kingdom;Knowledge Media Institute and Centre for Research in Computing, The Open University, Milton Keynes, United Kingdom

  • Venue:
  • Web Intelligence and Agent Systems
  • Year:
  • 2007

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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.