Revisiting logical imaging for information retrieval

Zuccon, Guido, Azzopardi, Leif, & van Rijsbergen, Cornelis J. (2009) Revisiting logical imaging for information retrieval. In Proceedings of 32nd international ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, Boston, MA, pp. 766-767.

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Abstract

Retrieval with Logical Imaging is derived from belief revision and provides a novel mechanism for estimating the relevance of a document through logical implication (i.e. P(q -> d)). In this poster, we perform the first comprehensive evaluation of Logical Imaging (LI) in Information Retrieval (IR) across several TREC test Collections. When compared against standard baseline models, we show that LI fails to improve performance. This failure can be attributed to a nuance within the model that means non-relevant documents are promoted in the ranking, while relevant documents are demoted. This is an important contribution because it not only contextualizes the effectiveness of LI, but crucially ex- plains why it fails. By addressing this nuance, future LI models could be significantly improved.

Impact and interest:

3 citations in Scopus
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ID Code: 69267
Item Type: Conference Paper
Refereed: Yes
DOI: 10.1145/1571941.1572118
ISBN: 9781605584836
Divisions: Current > Institutes > Institute for Future Environments
Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2009 ACM
Deposited On: 02 Jun 2014 00:59
Last Modified: 16 Jun 2014 23:30

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