Abstract
AbstractTraditional information search in which queries are posed against a known and rigid schema over a structured database is shifting towards a Web scenario in which exposed schemas are vague or absent and data comes from heterogeneous sources. In this framework, query answering cannot be precise and needs to be relaxed, with the goal of matching user requests with accessible data. In this paper, we propose a logical model and an abstract query language as a foundation for querying data sets with vague schemas. Our approach takes advantages of the availability of taxonomies, that is, simple classifications of terms arranged in a hierarchical structure. The model is a natural extension of the relational model in which data domains are organized in hierarchies, according to different levels of generalization. The query language is a conservative extension of relational algebra where special operators allow the specification of relaxed queries over vaguely structured information. We study equivalence and rewriting properties of the query language that can be used for query optimization.KeywordsQuery LanguageRelational AlgebraQuery OptimizationConservative ExtensionStandard SelectionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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