Community relation discovery by named entities

Jianhan Zhu, Alexandre L. Gonçalves, Victoria S. Uren, Enrico Motta, Roberto Pacheco, Dawei Song, Stefan Rüger

Research output: Chapter in Book/Published conference outputConference publication


Discovering who works with whom, on which projects and with which customers is a key task in knowledge management. Although most organizations keep models of organizational structures, these models do not necessarily accurately reflect the reality on the ground. In this paper we present a text mining method called CORDER which first recognizes named entities (NEs) of various types from Web pages, and then discovers relations from a target NE to other NEs which co-occur with it. We evaluated the method on our departmental Website. We used the CORDER method to first find related NEs of four types (organizations, people, projects, and research areas) from Web pages on the Website and then rank them according to their co-occurrence with each of the people in our department. 20 representative people were selected and each of them was presented with ranked lists of each type of NE. Each person specified whether these NEs were related to him/her and changed or confirmed their rankings. Our results indicate that the method can find the NEs with which these people are closely related and provide accurate rankings.
Original languageEnglish
Title of host publicationProceedings of the sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 19-22 August 2007
Number of pages8
ISBN (Electronic)978-1-4244-0973-0
Publication statusPublished - 2007
Event6th International Conference on Machine Learning and Cybernetics - Hong Kong, China
Duration: 19 Aug 200722 Aug 2007


Conference6th International Conference on Machine Learning and Cybernetics
CityHong Kong


  • relation discovery
  • ranking
  • similarities
  • named entity recognition
  • clustering


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