Despite the recent advances in search quality, the fast increase in the size of the Web collection has introduced new challenges for Web ranking algorithms. In fact, there are still many situations in which the users are presented with imprecise or very poor results. One of the key difficulties is the fact that users usually submit very short and ambiguous queries, and they do not fully specify their information needs. That is, it is necessary to improve the query formation process if better answers are to be provided. In this work we propose a novel concept-based query expansion technique, which allows disambiguating queries submitted to search engines. The concepts are extracted by analyzing and locating cycles in a special type of query relations graph. This is a directed graph built from query relations mined using association rules. The concepts related to the current query are then shown to the user who selects the one concept that he interprets is most related to his query. This concept is used to expand the original query and the expanded query is processed instead. Using a Web test collection, we show that our approach leads to gains in average precision figures of roughly 32%. Further, if the user also provides information on the type of relation between his query and the selected concept, the gains in average precision go up to roughly 52%.

Entity-relationship-structured data is becoming more important on the Web. For example, large knowledge bases have been automatically constructed by information extraction from Wikipedia and other Web sources. Entities and relationships can be represented by subject-property-object triples in the RDF model, and can then be precisely searched by structured query languages like SPARQL. Because of their Boolean-match semantics, such queries often return too few or even no results. To improve recall, it is thus desirable to support users by automatically relaxing or reformulating queries in such a way that the intention of the original user query is preserved while returning a sufficient number of ranked results. In this paper we describe comprehensive methods to relax SPARQL-like triple-pattern queries, possibly augmented with keywords, in a fully automated manner. Our framework produces a set of relaxations by means of statistical language models for structured RDF data and queries. The query

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