Formal models for expert finding in enterprise corpora

Balog, K., Azzopardi, L. and Rijke, M. (2006) Formal models for expert finding in enterprise corpora. In: Dumais, S., Efthimiadis, E.N., Hawking, D. and Järvelin, K. (eds.) SIGIR '06: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, University of Washington, Seattle, WA, USA. Association for Computing Machinery: New York, USA, pp. 43-50. ISBN 9781595933690 (doi: 10.1145/1148170.1148181)

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Abstract

Searching an organization's document repositories for experts provides a cost effective solution for the task of expert finding. We present two general strategies to expert searching given a document collection which are formalized using generative probabilistic models. The first of these directly models an expert's knowledge based on the documents that they are associated with, whilst the second locates documents on topic, and then finds the associated expert. Forming reliable associations is crucial to the performance of expert finding systems. Consequently, in our evaluation we compare the different approaches, exploring a variety of associations along with other operational parameters (such as topicality). Using the TREC Enterprise corpora, we show that the second strategy consistently outperforms the first. A comparison against other unsupervised techniques, reveals that our second model delivers excellent performance.

Item Type:Book Sections
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Azzopardi, Dr Leif
Authors: Balog, K., Azzopardi, L., and Rijke, M.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Publisher:Association for Computing Machinery
ISBN:9781595933690

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