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A Flexible Language for Exploring Clustered Search Results

A Flexible Language for Exploring Clustered Search Results

Gloria Bordogna, Alessandro Campi, Stefania Ronchi, Giuseppe Psaila
Copyright: © 2010 |Pages: 35
ISBN13: 9781605668581|ISBN10: 1605668583|ISBN13 Softcover: 9781616924478|EISBN13: 9781605668598
DOI: 10.4018/978-1-60566-858-1.ch007
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MLA

Bordogna, Gloria, et al. "A Flexible Language for Exploring Clustered Search Results." Scalable Fuzzy Algorithms for Data Management and Analysis: Methods and Design, edited by Anne Laurent and Marie-Jeanne Lesot, IGI Global, 2010, pp. 179-213. https://doi.org/10.4018/978-1-60566-858-1.ch007

APA

Bordogna, G., Campi, A., Ronchi, S., & Psaila, G. (2010). A Flexible Language for Exploring Clustered Search Results. In A. Laurent & M. Lesot (Eds.), Scalable Fuzzy Algorithms for Data Management and Analysis: Methods and Design (pp. 179-213). IGI Global. https://doi.org/10.4018/978-1-60566-858-1.ch007

Chicago

Bordogna, Gloria, et al. "A Flexible Language for Exploring Clustered Search Results." In Scalable Fuzzy Algorithms for Data Management and Analysis: Methods and Design, edited by Anne Laurent and Marie-Jeanne Lesot, 179-213. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-858-1.ch007

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Abstract

In this chapter the authors consider the problem of defining a flexible approach for exploring huge amounts of results retrieved by several Internet search services (like search engines). The goal is to offer users a way to discover relevant hidden relationships between documents. The proposal is motivated by the observation that visualization paradigms, based on either the ranked list or clustered results, do not allow users to fully appreciate and understand the retrieved contents. In the case of long ranked lists, the user generally analyzes only the first few pages. On the other side, in the case the documents are clustered, to understand their contents the user does not have other means that looking at the cluster labels. When the same query is submitted to distinct search services, they may produce partially overlapped clustered results, where clusters identified by distinct labels collect some common documents. Moreover, clusters with similar labels, but containing distinct documents, may be produced as well. In such a situation, it may be useful to compare, combine and rank the cluster contents, to filter out relevant documents. In this chapter the authors present a novel manipulation language, in which several operators (inspired by relational algebra) and distinct ranking methods can be exploited to analyze the clusters’ contents. New clusters can be generated and ranked based on distinct criteria, by combining (i.e., overlapping, refining and intersecting) clusters in a set oriented fashion. Specifically, the chapter is focused on the ranking methods defined for each operator of the language.

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