Community relation discovery by named entities

Jian Han Zhu; Alexandre L. Gonçalves; Victoria S. Uren; Enrico Motta; Roberto Pacheco; Da Wei Song; Stefan Rüger

doi:10.1109/ICMLC.2007.4370469

Community relation discovery by named entities

Jian Han Zhu^*, Alexandre L. Gonçalves, Victoria S. Uren, Enrico Motta, Roberto Pacheco, Da Wei Song, Stefan Rüger

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

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 language	English
Title of host publication	Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
Pages	1966-1973
Number of pages	8
DOIs	https://doi.org/10.1109/ICMLC.2007.4370469
Publication status	Published - 2007
Externally published	Yes
Event	6th International Conference on Machine Learning and Cybernetics, ICMLC 2007 - Hong Kong, China Duration: 19 Aug 2007 → 22 Aug 2007

Publication series

Name	Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
Volume	4

Conference

Conference	6th International Conference on Machine Learning and Cybernetics, ICMLC 2007
Country/Territory	China
City	Hong Kong
Period	19/08/07 → 22/08/07

Keywords

Clustering
Named entity recognition
Ranking
Relation discovery
Similarities

Access to Document

10.1109/ICMLC.2007.4370469

Cite this

Zhu, J. H., Gonçalves, A. L., Uren, V. S., Motta, E., Pacheco, R., Song, D. W., & Rüger, S. (2007). Community relation discovery by named entities. In Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007 (pp. 1966-1973). Article 4370469 (Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007; Vol. 4). https://doi.org/10.1109/ICMLC.2007.4370469

@inproceedings{7a0235fc368448f18e99c9d13f597308,

title = "Community relation discovery by named entities",

abstract = "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.",

keywords = "Clustering, Named entity recognition, Ranking, Relation discovery, Similarities",

author = "Zhu, {Jian Han} and Gon{\c c}alves, {Alexandre L.} and Uren, {Victoria S.} and Enrico Motta and Roberto Pacheco and Song, {Da Wei} and Stefan R{\"u}ger",

year = "2007",

doi = "10.1109/ICMLC.2007.4370469",

language = "English",

isbn = "142440973X",

series = "Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007",

pages = "1966--1973",

booktitle = "Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007",

note = "6th International Conference on Machine Learning and Cybernetics, ICMLC 2007 ; Conference date: 19-08-2007 Through 22-08-2007",

}

Zhu, JH, Gonçalves, AL, Uren, VS, Motta, E, Pacheco, R, Song, DW & Rüger, S 2007, Community relation discovery by named entities. in Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007., 4370469, Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007, vol. 4, pp. 1966-1973, 6th International Conference on Machine Learning and Cybernetics, ICMLC 2007, Hong Kong, China, 19/08/07. https://doi.org/10.1109/ICMLC.2007.4370469

Community relation discovery by named entities. / Zhu, Jian Han; Gonçalves, Alexandre L.; Uren, Victoria S. et al.
Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007. 2007. p. 1966-1973 4370469 (Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007; Vol. 4).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Community relation discovery by named entities

AU - Zhu, Jian Han

AU - Gonçalves, Alexandre L.

AU - Uren, Victoria S.

AU - Motta, Enrico

AU - Pacheco, Roberto

AU - Song, Da Wei

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 - Clustering

KW - Named entity recognition

KW - Ranking

KW - Relation discovery

KW - Similarities

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DO - 10.1109/ICMLC.2007.4370469

M3 - Conference contribution

AN - SCOPUS:38049083094

SN - 142440973X

SN - 9781424409730

T3 - Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007

SP - 1966

EP - 1973

BT - Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007

T2 - 6th International Conference on Machine Learning and Cybernetics, ICMLC 2007

Y2 - 19 August 2007 through 22 August 2007

ER -

Zhu JH, Gonçalves AL, Uren VS, Motta E, Pacheco R, Song DW et al. Community relation discovery by named entities. In Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007. 2007. p. 1966-1973. 4370469. (Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007). doi: 10.1109/ICMLC.2007.4370469

Community relation discovery by named entities

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Keywords

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