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Abstract

Recent IR extensions to XML query languages such as Xpath 1.0 Full-Text or the NEXI query language of the INEX benchmark series reflect the emerging interest in IR-style ranked retrieval over semistructured data. TopX is a top-$k$ retrieval engine for text and semistructured data. It terminates query execution as soon as it can safely determine the $k$ top-ranked result elements according to a monotonic score aggregation function with respect to a multidimensional query. It efficiently supports vague search on both content- and structure-oriented query conditions for dy\-namic query relaxation with controllable influence on the result ranking. The main contributions of this paper unfold into four main points: 1) fully implemented models and algorithms for ranked XML retrieval with XPath Full-Text functionality, 2) efficient and effective top-$k$ query processing for semistructured data, 3) support for integrating thesauri and ontologies with statistically quantified relationships among concepts, leveraged for word-sense disambiguation and \linebreak query expansion, and 4) a comprehensive description of the TopX system, with performance experiments on large-scale corpora like TREC Terabyte and INEX Wikipedia.