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      Certain Answers to a SPARQL Query over a Knowledge Base (extended version)

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          Abstract

          Ontology-Mediated Query Answering (OMQA) is a well-established framework to answer queries over an RDFS or OWL Knowledge Base (KB). OMQA was originally designed for unions of conjunctive queries (UCQs), and based on certain answers. More recently, OMQA has been extended to SPARQL queries, but to our knowledge, none of the efforts made in this direction (either in the literature, or the so-called SPARQL entailment regimes) is able to capture both certain answers for UCQs and the standard interpretation of SPARQL over a plain graph. We formalize these as requirements to be met by any semantics aiming at conciliating certain answers and SPARQL answers, and define three additional requirements, which generalize to KBs some basic properties of SPARQL answers. Then we show that a semantics can be defined that satisfies all requirements for SPARQL queries with SELECT, UNION, and OPTIONAL, and for DLs with the canonical model property. We also investigate combined complexity for query answering under such a semantics over DL-Lite R KBs. In particular, we show for different fragments of SPARQL that known upper-bounds for query answering over a plain graph are matched.

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          Tractable Reasoning and Efficient Query Answering in Description Logics: The DL-Lite Family

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            Semantics and complexity of SPARQL

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              Foundations of SPARQL query optimization

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                Author and article information

                Journal
                06 November 2019
                Article
                1911.02668
                039a0863-7592-4fa7-974e-4b62956bae5a

                http://creativecommons.org/licenses/by-nc-sa/4.0/

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                Custom metadata
                This is the extended version of a article published at the 9th Joint International Semantic Technology Conference (JIST 2019)
                cs.DB cs.AI

                Databases,Artificial intelligence
                Databases, Artificial intelligence

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