Broad expertise retrieval in sparse data environments

Balog, K., Bogers, T., Azzopardi, L., de Rijke, M. and van den Bosch, A. (2007) Broad expertise retrieval in sparse data environments. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Amsterdam, The Netherlands, 23-27 July 2007, pp. 551-558. ISBN 9781595935977 (doi: 10.1145/1277741.1277836)

Abstract

Expertise retrieval has been largely unexplored on data other than the W3C collection. At the same time, many intranets of universities and other knowledge-intensive organisations offer examples of relatively small but clean multilingual expertise data, covering broad ranges of expertise areas. We first present two main expertise retrieval tasks, along with a set of baseline approaches based on generative language modeling, aimed at finding expertise relations between topics and people. For our experimental evaluation, we introduce (and release) a new test set based on a crawl of a university site. Using this test set, we conduct two series of experiments. The first is aimed at determining the effectiveness of baseline expertise retrieval methods applied to the new test set. The second is aimed at assessing refined models that exploit characteristic features of the new test set, such as the organizational structure of the university, and the hierarchical structure of the topics in the test set. Expertise retrieval models are shown to be robust with respect to environments smaller than the W3C collection, and current techniques appear to be generalizable to other settings.

• Item Type: Conference Proceedings
• Additional Information: © ACM, 2007. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval http://doi.acm.org/10.1145/1277741.1277836
• Keywords: Expertise search, expert finding, intranet search, language models.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Azzopardi, Dr Leif
• Authors: Balog, K., Bogers, T., Azzopardi, L., de Rijke, M., and van den Bosch, A.
• Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
• College/School: College of Science and Engineering > School of Computing Science
• Publisher: ACM
• ISBN: 9781595935977
• Copyright Holders: Copyright © 2007 ACM
• First Published: First published in Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher.