Query exhaustivity, relevance feedback and search success in automatic and interactive query expansion

Abstract

This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractThis study explored how the expression of search facets and relevance feedback by users was related to search success in interactive and automatic query expansion in the course of the search process. Search success was measured both in the number of relevant documents retrieved and relevance scores of these items based on a four point scaling. Research design consisted of 26 users searching for four TREC topics in Okapi IR system, half using interactive and half automatic query expansion based on RF. The search logs were recorded, and the users filled in a questionnaire for each topic concerning various features of searching. The results showed that the exhaustivity of the query was the most significant predictor of search success, and that interactive expansion led to better search success than automatic one.2

How doctors search: A study of query behaviour and the impact on search results

“…This corresponds to Wolfram's (2008) findings that a smaller number of terms outperforms queries with a larger number of terms. However, it is in contrast to previous findings by Järvelin (1998), andVakkari et al (2004) that suggested that search success for precision-oriented searches depends on exhaustive coverage of search facets. The present findings indicate that a few, seemingly core search facets might be sufficient for retrieval success.…”

“…Altogether the findings about query structure, expansion and complexity imply that search success depends on the searchers' ability to articulate the facets of topics in query terms, structure the facets in the query, and cover the facets exhaustively. Vakkari et al (2004) suggest that a point of departure for improving best-match systems would be providing searchers in the initial search with an option to see the facets of the topic.…”

“…The study showed that increasing the exhaustivity of optimized best-match queries led to a significant increase in precision, especially for tasks that require high precision. Vakkari, Jones, and MacFarlane (2004) explored how the expression of search facets and relevance feedback by users were related to search success in automatic and interactive query expansion. In an experiment with 26 students using the Okapi system to search for 4 TREC topics, the results showed that search success was related both to how exhaustively search facets were covered by query terms and to the relevance feedback.…”

Conceptual language models for domain-specific retrieval

“…Interestingly, all of these approaches open up the door to providing conceptual relevance feedback to users. Instead of suggesting vocabulary terms that are related to the query, we can now suggest related concepts that can, for example, be used for navigational purposes (Keskustalo et al, 2008;Meij & de Rijke, 2007;Silveira & Ribeiro-Neto, 2004;Vakkari et al, 2004). Trajkova and Gauch (2004) describe another possible application; their system keeps track of a user's history by classifying visited web pages onto the concepts from the ODP.…”

“…The retrieved documents serve as examples to select additional query terms (Rocchio, 1971). Pseudo-relevance feedback methods assume the top-ranked documents to be relevant, but explicit or implicit relevance judgements from users may also be used (Anick, 2003;Keskustalo, Järvelin, & Pirkola, 2008;Vakkari, Jones, Macfarlane, & Sormunen, 2004;Xu & Croft, 1996). The recent interest of the semantic web community regarding models and methods related to ontologies have also sparked a renewed interest in using ontological information for relevance feedback (Bhogal, Macfarlane, & Smith, 2007;Rocha, Schwabe, & Aragao, 2004).…”