Answering SPARQL queries modulo RDF Schema with paths

Report, Preprint English OPEN
Alkhateeb , Faisal ; Euzenat , Jérôme (2013)
  • Publisher: HAL CCSD
  • Subject: Resource Description Framework (RDF) | Semantic web | [ INFO.INFO-WB ] Computer Science [cs]/Web | Regular expression | Computer Science - Databases | Path | nSPARQL | cpSPARQL | PSPARQL | Query modulo schema | [ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI] | ACM : I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.3: Deduction and Theorem Proving | SPARQL | Query language | RDF Schema | Constrained regular expression

alkhateeb2013a; 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.; SPARQL est le langage de requête standard pour interroger des graphes RDF. Dans son instanciation stricte, il ne propose que des requêtes en fonction de la sémantique de RDF et n'interprète donc pas les vocabulaires exprimés en RDFS ou OWL. Plusieurs extensions de SPARQL ont été proposées pour interroger les données RDF en fonction de vocabulaires RDFS et d'ontologies OWL. Par ailleurs, les extensions de SPARQL qui utilisent des expressions régulières pour naviguer dans les graphes RDF peuvent être utilisées pour répondre aux requêtes sous la sémantique de RDFS. Nous introduisons un cadre général qui permet d'exprimer d'une manière homogène l'interprétation de SPARQL en fonction de différentes sémantiques. Les extensions de SPARQL sont interprétées dans ce cadre et leurs inconvénients sont présentés. En particulier, nous montrons que le langage de requête PSPARQL, une extension stricte de SPARQL, permet de répondre aux requêtes SPARQL sous la sémantique de RDFS avec la même complexité que SPARQL par une transformation des requêtes. Nous considérons également CPSPARQL, une extension de PSPARQL, qui permet de poser des contraintes sur les chemins. Nous montrons que CPSPARQL est suffisamment expressif pour répondre aux requêtes PSPARQL et CPSPARQL sous la sémantique de RDFS. Nous présentons également nSPARQL, un langage de chemins inspiré de XPath permettant d'évaluer des requêtes sous la sémantique de RDFS. Nous comparons l'expressivité et la complexité de nSPARQL et le fragment correspondant de CPSPARQL, que nous appelons cpSPARQL. Les deux langages ont la même complexité bien que cpSPARQL, étant une extension stricte de SPARQL, soit plus expressif que nSPARQL.
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    e hp, sp, qi hq, sp, ri hp, sp, ri hA, sc, Bi hB, sc, Ci hA, sc, Ci hA, sc, Bi hx, type, Ai hx, type, Bi Algorithm 2 Eval(G, R, ha, bi)

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