Bibliography on psparql (2017-06-06)

The standard SPARQL query language is currently defined for querying RDF graphs without RDFS semantics. Several extensions of SPARQL to RDFS semantics have been proposed. 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. 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 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.

RDF is a knowledge representation language dedicated to the annotation of resources within the framework of the semantic web. Among the query languages for RDF, SPARQL allows querying RDF through graph patterns, i.e., RDF graphs involving variables. Other languages, inspired by the work in databases, use regular expressions for searching paths in RDF graphs. Each approach can express queries that are out of reach of the other one. Hence, we aim at combining these two approaches. For that purpose, we define a language, called PRDF (for "Path RDF") which extends RDF such that the arcs of a graph can be labeled by regular expression patterns. We provide PRDF with a semantics extending that of RDF, and propose a correct and complete algorithm which, by computing a particular graph homomorphism, decides the consequence between an RDF graph and a PRDF graph. We then define the PSPARQL query language, extending SPARQL with PRDF graph patterns and complying with RDF model theoretic semantics. PRDF thus offers both graph patterns and path expressions. We show that this extension does not increase the computational complexity of SPARQL and, based on the proposed algorithm, we have implemented a correct and complete PSPARQL query engine.

We have proposed an extension of SPARQL, called PSPARQL, to characterize paths of variable lengths in an RDF knowledge base (e.g. "Does there exists a trip from town A to town B?"). However, PSPARQL queries do not allow expressing constraints on internal nodes (e.g. "Moreover, one of the stops must provide a wireless access."). This paper proposes an extension of PSPARQL, called CPSPARQL, that allows expressing constraints on paths. For this extension, we provide an abstract syntax, semantics as well as a sound and complete inference mechanism for answering CPSPARQL queries.

RDF is a knowledge representation language dedicated to the annotation of resources within the Semantic Web. Though RDF itself can be used as a query language for an RDF knowledge base (using RDF semantic consequence), the need for added expressivity in queries has led to define the SPARQL query language. SPARQL queries are defined on top of graph patterns that are basically RDF graphs with variables. SPARQL queries remain limited as they do not allow queries with unbounded sequences of relations (e.g. "does there exist a trip from town A to town B using only trains or buses?"). We show that it is possible to extend the RDF syntax and semantics defining the PRDF language (for Path RDF) such that SPARQL can overcome this limitation by simply replacing the basic graph patterns with PRDF graphs, effectively mixing RDF reasoning with database-inspired regular paths. We further extend PRDF to CPRDF (for Constrained Path RDF) to allow expressing constraints on the nodes of traversed paths (e.g. "Moreover, one of the correspondences must provide a wireless connection."). We have provided sound and complete algorithms for answering queries (the query is a PRDF or a CPRDF graph, the knowledge base is an RDF graph) based upon a kind of graph homomorphism, along with a detailed complexity analysis. Finally, we use PRDF or CPRDF graphs to generalize SPARQL graph patterns, defining the PSPARQL and CPSPARQL extensions, and provide experimental tests using a complete implementation of these two query languages.

Solving problems raised by heterogeneous ontologies can be achieved by matching the ontologies and processing the resulting alignments. This is typical of data mediation in which the data must be translated from one knowledge source to another. We propose to solve the data translation problem, i.e. the processing part, using the SPARQL query language. Indeed, such a language is particularly adequate for extracting data from one ontology and, through its CONSTRUCT statement, for generating new data. We present examples of such transformations, but we also present a set of example correspondences illustrating the needs for particular representation constructs, such as aggregates, value-generating built-in functions and paths, which are missing from SPARQL. Hence, we advocate the use of two SPARQL extensions providing these missing features.

RDF is a knowledge representation language dedicated to the annotation of resources within the framework of the semantic web. Among the query languages for querying an RDF knowledge base, some, such as SPARQL, are based on the formal semantics of RDF and the concept of semantic consequence, others, inspired by the work in databases, use regular expressions making it possible to search the paths in the graph associated with the knowledge base. In order to combine the expressivity of these two approaches, we define a mixed language, called PRDF (for "Paths RDF") in which the arcs of a graph can be labeled by regular expressions. We define the syntax and the semantics of these objects, and propose a correct and complete algorithm which, by a kind of homomorphism, calculates the semantic consequence between an RDF graph and a PRDF graph. This algorithm is the heart of query answering for the PSPARQL query language, the extension of the SPARQL query language which we propose and have implemented: a PSPARQL query allows to query an RDF knowledge base using graph patterns whose predicates are regular expressions.

RDF est un langage de représentation de connaissances dédié à l'annotation de ressources dans le cadre du web sémantique. Parmi les langages de requêtes permettant d'interroger une base de connaissances RDF, certains, tels que SPARQL, s'appuient sur la sémantique formelle de RDF et la notion de conséquence sémantique, d'autres, inspirés par des travaux en bases de données, utilisent des expressions régulières permettant de chercher des chemins dans le graphe associé à la base de connaissances. Afin de conjuguer l'expressivité de ces deux approches, nous définissons un langage mixte, appelé PRDF (pour "Paths RDF") dans lequel les arcs d'un graphe peuvent être étiquetés par des expressions régulières. Nous définissons la syntaxe et la sémantique de PRDF, et proposons un algorithme correct et complet qui, par un homomorphisme particulière, calcule la conséquence sémantique entre un graphe RDF et un graphe PRDF. Cet algorithme est au c\oe{}ur de l'extension du langage de requêtes SPARQL que nous proposons et avons implémenté: une requête PSPARQL permet d'interroger une base de connaissances RDF en utilisant des patterns dont les prédicats sont des expressions régulières.

RDF is a knowledge representation language dedicated to the annotation of resources within the Semantic Web. Though RDF itself can be used as a query language for an RDF knowledge base (using RDF consequence), the need for added expressivity in queries has led to the definition of the SPARQL query language. SPARQL queries are defined on top of graph patterns that are basically RDF (and more precisely GRDF) graphs. To be able to characterize paths of arbitrary length in a query (e.g., "does there exist a trip from town A to town B using only trains and buses?"), we have already proposed the PRDF (for Path RDF) language, effectively mixing RDF reasonings with database-inspired regular paths. However, these queries do not allow expressing constraints on the internal nodes (e.g., "Moreover, one of the stops must provide a wireless connection."). To express these constraints, we present here an extension of RDF, called CPRDF (for Constrained paths RDF). For this extension of RDF, we provide an abstract syntax and an extension of RDF semantics. We characterize query answering (the query is a CPRDF graph, the knowledge base is an RDF graph) as a particular case of CPRDF entailment that can be computed using some kind of graph homomorphism. Finally, we use CPRDF graphs to generalize SPARQL graph patterns, defining the CPSPARQL extension of that query language, and prove that the problem of query answering using only CPRDF graphs is an NP-hard problem, and query answering thus remains a PSPACE-complete problem for CPSPARQL.