Broad expertise retrieval in sparse data environments

Krisztian Balog, Toine Bogers, Leif Azzopardi, Maarten de Rijke, Antal van den Bosch

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

100 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationSIGIR '07 Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
Place of PublicationNew York, NY, USA
Pages551-558
Number of pages8
DOIs
Publication statusPublished - 23 Jul 2007

Keywords

  • expert finding
  • language models
  • intranet search
  • expertise search

Cite this

Balog, K., Bogers, T., Azzopardi, L., de Rijke, M., & van den Bosch, A. (2007). Broad expertise retrieval in sparse data environments. In SIGIR '07 Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 551-558). https://doi.org/10.1145/1277741.1277836