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Community Relation Discovery by Named Entities

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

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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.

Item Type: Conference or Workshop Item
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Research Group: Centre for Research in Computing (CRC)
Item ID: 11969
Depositing User: Users 8580 not found.
Date Deposited: 09 Oct 2008 12:55
Last Modified: 07 Dec 2018 09:13
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