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 explains why it fails. By addressing this nuance, future LI models could be significantly improved.
- G. Amati and S. Kerpedjiev. An Information Retrieval Logic Model: Implementation and Experiments. Technical Report Rel5B04892, FUB, Italy, 1992.Google Scholar
- F. Crestani, I. Ruthven, M. Sanderson, and C.J. van Rijsbergen. The Troubles with Using a Logical Model of IR on a Large Collection of Documents. In Proc. TREC4, pages 509--526, 1995.Google Scholar
- F. Crestani and C.J. van Rijsbergen. Probability Kinematics in Information Retrieval. Proc. ACM SIGIR, pages 291--299, 1995. Google ScholarDigital Library
- C.J. van Rijsbergen. A new Theoretical Framework for Information Retrieval. Proc. ACM SIGIR, pages 194--200, 1986. Google ScholarDigital Library
Index Terms
- Revisiting logical imaging for information retrieval
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