On the SPARQL Direct Semantics Entailment Regime for OWL 2 QL

OWL 2 QL is the profile of OWL 2 targeted to Ontology-Based Data Access (OBDA) scenarios, where large amount of data are to be accessed and thus query answering is required to be especially efficient in the size of such data, namely AC0 in data complexity. On the other hand, the syntax and the semantics of the SPARQL query language for OWL 2 is defined by means of the Direct Semantics Entailment Regime (DSER), which considers queries including any assertion expressible in the language of the queried ontology, i.e., both ABox atoms, TBox atoms and inequalities expressed by means of DifferentIndividuals atoms. Thus, in this paper, we investigate query answering over OWL 2 QL under DSER. In particular, we show that, by virtue of the restricted meaning assigned to existential variables and union, query answering can be reduced to the evaluation of a Datalog program. Finally, we investigate query answering under a new SPARQL entailment regime, called Direct Semantics Answering Regime (DSAR), obtained by modifying DSER in such a way that existentially quantified variables are assigned the classical logical meaning, and provide an algorithm for answering queries over OWL 2 QL ontologies under DSAR, that is AC0 in data complexity, for a class of queries comprising both TBox atoms, ABox atoms and inequalities

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Answering SPARQL queries modulo RDF Schema with paths

SPARQL is the standard query language for RDF graphs. In its strict instantiation, it only offers querying according to the RDF semantics and would thus ignore the semantics of data expressed with respect to (RDF) schemas or (OWL) ontologies. Several extensions to SPARQL have been proposed to query RDF data modulo RDFS, i.e., interpreting the query with RDFS semantics and/or considering external ontologies. We introduce a general framework which allows for expressing query answering modulo a particular semantics in an homogeneous way. In this paper, we discuss extensions of SPARQL that use regular expressions to navigate RDF graphs and may be used to answer queries considering RDFS semantics. We also consider their embedding as extensions of SPARQL. These SPARQL extensions are interpreted within the proposed framework and their drawbacks are presented. In particular, we show that the PSPARQL query language, a strict extension of SPARQL offering transitive closure, allows for answering SPARQL queries modulo RDFS graphs with the same complexity as SPARQL through a simple transformation of the queries. We also consider languages which, in addition to paths, provide constraints. In particular, we present and compare nSPARQL and our proposal CPSPARQL. We show that CPSPARQL is expressive enough to answer full SPARQL queries modulo RDFS. Finally, we compare the expressiveness and complexity of both nSPARQL and the corresponding fragment of CPSPARQL, that we call cpSPARQL. We show that both languages have the same complexity through cpSPARQL, being a proper extension of SPARQL graph patterns, is more expressive than nSPARQL.Comment: RR-8394; alkhateeb2003

From Questions to Effective Answers: On the Utility of Knowledge-Driven Querying Systems for Life Sciences Data

We compare two distinct approaches for querying data in the context of the life sciences. The first approach utilizes conventional databases to store the data and intuitive form-based interfaces to facilitate easy querying of the data. These interfaces could be seen as implementing a set of "pre-canned" queries commonly used by the life science researchers that we study. The second approach is based on semantic Web technologies and is knowledge (model) driven. It utilizes a large OWL ontology and same datasets as before but associated as RDF instances of the ontology concepts. An intuitive interface is provided that allows the formulation of RDF triples-based queries. Both these approaches are being used in parallel by a team of cell biologists in their daily research activities, with the objective of gradually replacing the conventional approach with the knowledge-driven one. This provides us with a valuable opportunity to compare and qualitatively evaluate the two approaches. We describe several benefits of the knowledge-driven approach in comparison to the traditional way of accessing data, and highlight a few limitations as well. We believe that our analysis not only explicitly highlights the specific benefits and limitations of semantic Web technologies in our context but also contributes toward effective ways of translating a question in a researcher's mind into precise computational queries with the intent of obtaining effective answers from the data. While researchers often assume the benefits of semantic Web technologies, we explicitly illustrate these in practice