Answering queries over an incomplete database w.r.t. a set of constraints is an important computational task with applications in fields as diverse as information integration and metadata management in the semantic Web. Description Logics (DLs) are constraint languages that have been extensively studied with the goal of providing useful modeling constructs while keeping the query answering problem decidable. For many DLs, query answering under constraints can be solved via query rewriting: given a conjunctive query Q and a set of DL constraints T , the query Q can be transformed into a datalog query Q T that takes into account the semantic consequences of T ; then, to obtain answers to Q w.r.t. T and some (arbitrary) database instance A, one can simply evaluate Q T over A using existing (deductive) database technology, without taking T into account. In this paper, we present a novel query rewriting algorithm that handles constraints modeled in the DL ELHIO ¬ and use it to show that answering conjunctive queries in this setting is PTime-complete w.r.t. data complexity. Our algorithm deals with various description logics of the EL and DL-Lite families and is worst-case optimal w.r.t. data complexity for all of them.

Abstract. The QL profile of OWL 2 has been designed so that it is possible to use database technology for query answering via query rewriting. We present a comparison of our resolution based rewriting algorithm with the standard algorithm proposed by Calvanese et al., implementing both and conducting an empirical evaluation using ontologies and queries derived from realistic applications. The results indicate that our algorithm produces significantly smaller rewritings in most cases, which could be important for practicality in realistic applications.

Abstract. We consider the problems of conjunctive query answering and rewriting for information integration systems in which a Description Logic ontology is used to provide a global view of the data. We present a resolution-based query rewriting algorithm for DL-Lite + ontologies, and use it to show that query answering in this setting is NLogSpacecomplete with respect to data complexity. We also show that our algorithm produces an optimal rewriting when the input ontology is expressed in the language DL-Lite. Finally, we sketch an extended version of the algorithm that would, we are confident, be optimal for several DL languages with data complexity of query answering ranging from LogSpace to PTime-complete.

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