The impact of query structure and query expansion on retrieval performance

Kekäläinen, Jaana; Järvelin, Kalervo

doi:10.1145/290941.290978

Cited by 70 publications

(62 citation statements)

References 21 publications

(14 reference statements)

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“…This is in line with the findings from laboratory experiments, which show that structured queries perform better than weakly structured ones (Hawking & al. 1997;Kekäläinen 1999;Kekäläinen & Järvelin 1998). This suggestion fits also with the finding that highly relevant documents benefit essentially more from the concept-based query expansion in ranking than marginally relevant ones (Sormunen & al.…”

Section: Discussionsupporting

confidence: 66%

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

Vakkari

Jones

MacFarlane

et al. 2004

Journal of Documentation

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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

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Section: Discussionsupporting

confidence: 66%

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

Vakkari

Jones

MacFarlane

et al. 2004

Journal of Documentation

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“…In [Kekäläinen and Järvelin 1998], it was shown that the probabilistic AND operator, in combination with maximally expanded query aspects, was very effective for query expansion. Nowadays, there are several search query languages that allow specification of general concepts including Boolean filtering, phrase matching, and term proximity, among others.…”

Section: Query Reformulationmentioning

confidence: 99%

A Survey of Automatic Query Expansion in Information Retrieval

2012

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The relative ineffectiveness of information retrieval systems is largely caused by the inaccuracy with which a query formed by a few keywords models the actual user information need. One well known method to overcome this limitation is automatic query expansion (AQE), whereby the user's original query is augmented by new features with a similar meaning. AQE has a long history in the information retrieval community but it is only in the last years that it has reached a level of scientific and experimental maturity, especially in laboratory settings such as TREC. This survey presents a unified view of a large number of recent approaches to AQE that leverage various data sources and employ very different principles and techniques. The following questions are addressed: Why is query expansion so important to improve search effectiveness? What are the main steps involved in the design and implementation of an AQE component? What approaches to AQE are available and how do they compare? Which issues must still be resolved before AQE becomes a standard component of large operational information retrieval systems (e.g., search engines)?

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“…An analysis at the RIA workshop [2] showed that a significant number of retrieval failures could be attributed to incomplete aspect coverage by the retrieval model. Kekäläinen and Järvelin [9] showed that one of the most effective structured query operators for query expansion was the probabilistic AND operator, in combination with maximally expanded query aspects. We therefore modified the expansion formula from the baseline to use #wand instead of #weight in the expanded query portion to combine the aspect-based expansion terms.…”

Section: Aspect-based Expansionmentioning

confidence: 99%

Query expansion using random walk models

Collins-Thompson

Callan

2005

Proceedings of the 14th ACM International Conference on Information and Knowledge Management

122

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It has long been recognized that capturing term relationships is an important aspect of information retrieval. Even with large amounts of data, we usually only have significant evidence for a fraction of all potential term pairs. It is therefore important to consider whether multiple sources of evidence may be combined to predict term relations more accurately. This is particularly important when trying to predict the probability of relevance of a set of terms given a query, which may involve both lexical and semantic relations between the terms.We describe a Markov chain framework that combines multiple sources of knowledge on term associations. The stationary distribution of the model is used to obtain probability estimates that a potential expansion term reflects aspects of the original query. We use this model for query expansion and evaluate the effectiveness of the model by examining the accuracy and robustness of the expansion methods, and investigate the relative effectiveness of various sources of term evidence. Statistically significant differences in accuracy were observed depending on the weighting of evidence in the random walk. For example, using co-occurrence data later in the walk was generally better than using it early, suggesting further improvements in effectiveness may be possible by learning walk behaviors.

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The impact of query structure and query expansion on retrieval performance

Cited by 70 publications

References 21 publications

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

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

A Survey of Automatic Query Expansion in Information Retrieval

Query expansion using random walk models

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