Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 19-22 August 2007 |
| Publisher | IEEE |
| Pages | 1966-1973 |
| Number of pages | 8 |
| ISBN (Electronic) | 978-1-4244-0973-0 |
| DOIs | |
| Publication status | Published - 2007 |
| Event | 6th International Conference on Machine Learning and Cybernetics - Hong Kong, China Duration: 19 Aug 2007 → 22 Aug 2007 |
Conference
| Conference | 6th International Conference on Machine Learning and Cybernetics |
|---|---|
| Country | China |
| City | Hong Kong |
| Period | 19/08/07 → 22/08/07 |
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Keywords
- relation discovery
- ranking
- similarities
- named entity recognition
- clustering
Cite this
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Community relation discovery by named entities. / Zhu, Jianhan; Gonçalves, Alexandre L.; Uren, Victoria S.; Motta, Enrico; Pacheco, Roberto ; Song, Dawei; Rüger, Stefan.
Proceedings of the sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 19-22 August 2007. IEEE, 2007. p. 1966-1973.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Community relation discovery by named entities
AU - Zhu, Jianhan
AU - Gonçalves, Alexandre L.
AU - Uren, Victoria S.
AU - Motta, Enrico
AU - Pacheco, Roberto
AU - Song, Dawei
AU - Rüger, Stefan
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
KW - relation discovery
KW - ranking
KW - similarities
KW - named entity recognition
KW - clustering
UR - http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4370469
U2 - 10.1109/ICMLC.2007.4370469
DO - 10.1109/ICMLC.2007.4370469
M3 - Conference contribution
SP - 1966
EP - 1973
BT - Proceedings of the sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 19-22 August 2007
PB - IEEE
ER -