Ranking Refinement via Relevance Feedback in Geographic Information Retrieval

Authors:
Esaú Villatoro-Tello;Luis Villaseñor-Pineda;Manuel Montes-Y-Gómez
Affiliations:
Laboratory of Language Technologies, Department of Computational Sciences, National Institute of Astrophysics, Optics and Electronics (INAOE), Mexico;Laboratory of Language Technologies, Department of Computational Sciences, National Institute of Astrophysics, Optics and Electronics (INAOE), Mexico;Laboratory of Language Technologies, Department of Computational Sciences, National Institute of Astrophysics, Optics and Electronics (INAOE), Mexico
Venue:
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Year:
2009

Citing 11
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Abstract

Recent evaluation results from Geographic Information Retrieval (GIR) indicate that current information retrieval methods are effective to retrieve relevant documents for geographic queries, but they have severe difficulties to generate a pertinent ranking of them. Motivated by these results in this paper we present a novel re-ranking method, which employs information obtained through a relevance feedback process to perform a ranking refinement . Performed experiments show that the proposed method allows to improve the generated ranking from a traditional IR machine, as well as results from traditional re-ranking strategies such as query expansion via relevance feedback.