Content-Oriented Relevance Feedback in XML-IR Using the Garnata Information Retrieval System

Authors:
Luis M. Campos;Juan M. Fernández-Luna;Juan F. Huete;Carlos Martín-Dancausa
Affiliations:
Departamento de Ciencias de la Computación e Inteligencia Artificial E.T.S.I. Informática y de Telecomunicación, CITIC-UGR, Universidad de Granada, Granada, Spain 18071;Departamento de Ciencias de la Computación e Inteligencia Artificial E.T.S.I. Informática y de Telecomunicación, CITIC-UGR, Universidad de Granada, Granada, Spain 18071;Departamento de Ciencias de la Computación e Inteligencia Artificial E.T.S.I. Informática y de Telecomunicación, CITIC-UGR, Universidad de Granada, Granada, Spain 18071;Departamento de Ciencias de la Computación e Inteligencia Artificial E.T.S.I. Informática y de Telecomunicación, CITIC-UGR, Universidad de Granada, Granada, Spain 18071
Venue:
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
Year:
2009

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Abstract

Relevance Feedback (RF) is a technique allowing to enrich an initial query according to the user feedback in order to get results closer to the user's information need. This paper presents a new RF method for keyword queries (content queries). It is based on the re-weighting of the original query terms plus the addition of new query terms from the content of elements jugded as relevant or non-relevant by the user. This RF method is integrated in our search engine, Garnata, and evaluated with the INEX 2007 collection.