Mining web data for competency management

J. Zhu; A.L. Goncalves; V.S. Uren; E. Motta; R. Pacheco

doi:10.1109/WI.2005.99

Mining web data for competency management

J. Zhu, A.L. Goncalves, V.S. Uren, E. Motta, R. Pacheco

Operations & Information Management

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

We present CORDER (COmmunity Relation Discovery by named Entity Recognition) an un-supervised machine learning algorithm that exploits named entity recognition and co-occurrence data to associate individuals in an organization with their expertise and associates. We discuss the problems associated with evaluating unsupervised learners and report our initial evaluation experiments.

Original language	English
Title of host publication	Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)
Editors	Andrzej Skowron, Rakesh Agrawal, Michael Luck, Takahira Yamaguchi, Pierre Morizet-Mahoudeaux, Jiming Liu, Ning Zhong
Place of Publication	Washington, DC (US)
Publisher	IEEE
Pages	94-100
Number of pages	7
ISBN (Print)	0-7695-2415-X
DOIs	https://doi.org/10.1109/WI.2005.99
Publication status	Published - 2005
Event	2005 IEEE/WIC/ACM International Conference on Web Intelligence - Compiegne, France Duration: 19 Sept 2005 → 22 Sept 2005

Conference

Conference	2005 IEEE/WIC/ACM International Conference on Web Intelligence
Abbreviated title	WI'05
Country/Territory	France
City	Compiegne
Period	19/09/05 → 22/09/05

Access to Document

10.1109/WI.2005.99

Cite this

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title = "Mining web data for competency management",

abstract = "We present CORDER (COmmunity Relation Discovery by named Entity Recognition) an un-supervised machine learning algorithm that exploits named entity recognition and co-occurrence data to associate individuals in an organization with their expertise and associates. We discuss the problems associated with evaluating unsupervised learners and report our initial evaluation experiments.",

author = "J. Zhu and A.L. Goncalves and V.S. Uren and E. Motta and R. Pacheco",

year = "2005",

doi = "10.1109/WI.2005.99",

language = "English",

isbn = "0-7695-2415-X",

pages = "94--100",

editor = "Andrzej Skowron and Rakesh Agrawal and Michael Luck and Takahira Yamaguchi and Pierre Morizet-Mahoudeaux and Jiming Liu and Ning Zhong",

booktitle = "Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)",

publisher = "IEEE",

address = "United States",

note = "2005 IEEE/WIC/ACM International Conference on Web Intelligence, WI'05 ; Conference date: 19-09-2005 Through 22-09-2005",

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Zhu, J, Goncalves, AL, Uren, VS, Motta, E & Pacheco, R 2005, Mining web data for competency management. in A Skowron, R Agrawal, M Luck, T Yamaguchi, P Morizet-Mahoudeaux, J Liu & N Zhong (eds), Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05). IEEE, Washington, DC (US), pp. 94-100, 2005 IEEE/WIC/ACM International Conference on Web Intelligence, Compiegne, France, 19/09/05. https://doi.org/10.1109/WI.2005.99

Mining web data for competency management. / Zhu, J.; Goncalves, A.L.; Uren, V.S. et al.
Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05). ed. / Andrzej Skowron; Rakesh Agrawal; Michael Luck; Takahira Yamaguchi; Pierre Morizet-Mahoudeaux; Jiming Liu; Ning Zhong. Washington, DC (US): IEEE, 2005. p. 94-100.

Research output: Chapter in Book/Published conference output › Conference publication

TY - GEN

T1 - Mining web data for competency management

AU - Zhu, J.

AU - Goncalves, A.L.

AU - Uren, V.S.

AU - Motta, E.

AU - Pacheco, R.

PY - 2005

Y1 - 2005

N2 - We present CORDER (COmmunity Relation Discovery by named Entity Recognition) an un-supervised machine learning algorithm that exploits named entity recognition and co-occurrence data to associate individuals in an organization with their expertise and associates. We discuss the problems associated with evaluating unsupervised learners and report our initial evaluation experiments.

AB - We present CORDER (COmmunity Relation Discovery by named Entity Recognition) an un-supervised machine learning algorithm that exploits named entity recognition and co-occurrence data to associate individuals in an organization with their expertise and associates. We discuss the problems associated with evaluating unsupervised learners and report our initial evaluation experiments.

UR - http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1517824

U2 - 10.1109/WI.2005.99

DO - 10.1109/WI.2005.99

M3 - Conference publication

SN - 0-7695-2415-X

SP - 94

EP - 100

BT - Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)

A2 - Skowron, Andrzej

A2 - Agrawal, Rakesh

A2 - Luck, Michael

A2 - Yamaguchi, Takahira

A2 - Morizet-Mahoudeaux, Pierre

A2 - Liu, Jiming

A2 - Zhong, Ning

PB - IEEE

CY - Washington, DC (US)

T2 - 2005 IEEE/WIC/ACM International Conference on Web Intelligence

Y2 - 19 September 2005 through 22 September 2005

ER -

Mining web data for competency management

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