Zhu, Jian-Han; Goncalves, Alexandre L.; Uren, Victoria; Motta, Enrico; Pacheco, Roberto; Song, Dawei and Rüger, Stefan
Community Relation Discovery by Named Entities.
In: Sixth International Conference on Machine Learning and Cybernetics, 19-22 August 2007, Hong Kong, p. 1966.
iscovering 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.
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