KGRAM Versatile Inference and Query Engine for the Web of Linked Data

International audienceQuerying and linking distributed and heterogeneous databases is increasingly needed, as plentiful data resources are published over the Web. This work describes the design of a versatile query system named KGRAM that supports (i) multiple query languages among which the SPARQL 1.1 standard, (ii) federation of multiple heterogeneous and distributed data sources, and (iii) adaptability to various data manipulation use cases. KGRAM provides abstractions for both the query language and the data model, thus delivering unifying reasoning mechanisms. It is implemented as a modular software suite to ease architecting and deploying dedicated data manipulation platforms. Its design integrates optimization concerns to deliver high query performance. Both KGRAM's software versatility and performance are evaluated

KGRAM Versatile Inference and Query Engine for the Web of Linked Data

International audienceQuerying and linking distributed and heterogeneous databases is increasingly needed, as plentiful data resources are published over the Web. This work describes the design of a versatile query system named KGRAM that supports (i) multiple query languages among which the SPARQL 1.1 standard, (ii) federation of multiple heterogeneous and distributed data sources, and (iii) adaptability to various data manipulation use cases. KGRAM provides abstractions for both the query language and the data model, thus delivering unifying reasoning mechanisms. It is implemented as a modular software suite to ease architecting and deploying dedicated data manipulation platforms. Its design integrates optimization concerns to deliver high query performance. Both KGRAM's software versatility and performance are evaluated

A Generic RDF Transformation Software and its Application to an Online Translation Service for Common Languages of Linked Data

International audienceIn this article we present a generic template and software solution for developers to support the many cases where we need to transform RDF. It relies on the SPARQL Template Transformation Language (STTL) which enables Semantic Web developers to write specific yet compact RDF transformers toward other languages and formats. We first briefly recall the STTL principles and software features. We then demonstrate the support it provides to programmers by presenting a selection of STTL-based RDF transformers for common languages. The software is available online as a Web service and all the RDF transformers presented in this paper can be tested online

Un moteur de traitement de requêtes SPARQL distribuées optimisée pour les partitions de données verticales et horizontales

National audienceAn increasing number of linked knowledge bases are openly accessible over the Internet. Distributed Query Processing (DQP) techniques enable querying multiple knowledge bases coherently. However, the precise DQP semantics is often overlooked, and query performance issues arise. In this paper, we propose a DQP engine for distributed RDF graphs, adopting a SPARQL-compliant DQP semantics. We improve performance through heuristics that generate Basic Graph Pattern-based sub-queries designed to maximise the parts of the query processed by the remote endpoints. We evaluate our DQP engine considering a query set representative of most common SPARQL clauses and different data distribution schemes. Results show a significant reduction of the number of remote queries executed and the query execution time while preserving completeness.Un nombre grandissant de bases de connaissances liées sont exposéesexposéesà travers l'Internet. Le traitement de requêtes distribuées (DQP) permet d'interroger des bases de connais-sances multiples simultanément. Cependant, la sémantique DQP précise est souvent négligée, et desprobì emes de performance doiventêtredoiventêtre traités. Dans ce papier, nous proposons un moteur DQP pour l'interrogation de graphs RDF distribués, conformè a la sé-mantique de SPARQL. Nous en améliorons la performance grâcè a des heuristiques qui génèrent des sous-requêtesrequêtes`requêtesà par-tir de schémas de graphes basiques (BGPs) demanì erè a maximiser la partie de la requête traitée par les serveurs de données distants. NousévaluonsNousévaluons notre moteur DQPàDQP`DQPà travers un ensemble de reqêtes représentatives de clauses SPARQL les plus répen-dues et des schémas de distribution des données divers. Les résultats montrent un réduction significative du nombre de requêtes exécutées et du temps de traitement sans altération de la complétude des résultats

SPARQL Template: A Transformation Language for RDF

RDF can be viewed as a meta-model to represent on the Web other languages and models, and in particular their abstract graph structure. The general research question addressed in this document is "How to transform RDF into other languages" and, in particular, how to generate the concrete syntax of expressions of a given language from their RDF representation. We show how SPARQL can be used as a generic RDF transformation rule language, independant from the output language. We define an RDF transformer as a set of transformation rules processed by a generic transformation rule engine. We present a lightweight syntactic extension to SPARQL in order to facilitate the writing of transformation rules and an implementation of a generic transformation rule engine. We show the feasability of our approach with several RDF transformers we have defined for various output languages

STTL: A SPARQL-based Transformation Language for RDF

International audienceThe general research question addressed in this paper is: "How to transform RDF into other languages". This is of prime interest to present data selected and extracted from the Web of data in a format suitable for the user (e.g., HTML or CSV). Moreover, RDF can be viewed as a meta-model to represent on the Web of data other languages and models. The above research question then becomes: "How to generate the concrete syntax of expressions of a given language from their RDF representation". To answer these questions, we present SPARQL Template Transformation Language (STTL), a generic RDF transformation rule language, independent from the output language. We conceived it as a lightweight syntactic extension to SPARQL and we show how to compile it into standard SPARQL. We present a generic transformation rule engine implementing STTL and several RDF transformers we defined for various output languages, showing STTL's expressive power

SPARQL Template: A Transformation Language for RDF

RDF can be viewed as a meta-model to represent on the Web other languages and models, and in particular their abstract graph structure. The general research question addressed in this document is "How to transform RDF into other languages" and, in particular, how to generate the concrete syntax of expressions of a given language from their RDF representation. We show how SPARQL can be used as a generic RDF transformation rule language, independant from the output language. We define an RDF transformer as a set of transformation rules processed by a generic transformation rule engine. We present a lightweight syntactic extension to SPARQL in order to facilitate the writing of transformation rules and an implementation of a generic transformation rule engine. We show the feasability of our approach with several RDF transformers we have defined for various output languages

Partage et réutilisation de règles sur le Web de données

In this thesis we address the problem of publishing, sharing and reusing rules on the Web of data. The approach adopted for sharing rules on the Web is to consider rule bases as particular data sources. Thus, we propose to publish rules using RDF, the standard representation language on the Web of data. We use the standard SPARQL language to query these particular RDF data that represent. rules. We propose a translation of a subset of RIF (Rule Interchange Format), the W3C standard for the exchange of rules on the Web, into a subset of SPARQL queries. Then we use the SPIN language for translating these SPARQL representations of rules into RDF.In other words, we consider the problem of publishing and reusing rules on the Web as a classical problem in knowledge engineering: sharing and reusing knowledge. We propose an approach based on (1) the representation in RDF of rule content and metadata, (2) the interoperability of this representation with the W3C recommendation RIF, (3) the publication of rules on the Web of data and (4) reusing rules by querying RDF data sources representing them with the SPARQL query language. We built a set of SPARQL queries enabling (1) to build specific rule bases for a given context or application, (2) to optimize inference engines based on rule selection with respect to target RDF data sources, (3) to validate and update rule bases. We use the Corese/KGRAM semantic engine to implement and evaluate our proposals.Dans cette thèse nous nous intéressons à la publication, au partage et à la réutilisation de règles sur le Web de données. L'approche que nous avons adoptée pour permettre la réutilisation de règles sur le Web, est de considérer des bases de règles comme des sources de données particulières. Il s'agit de les publier dans le langage RDF, le standard de représentation du Web de données. Nous utilisons des requêtes SPARQL, le standard du Web de données, pour interroger ces données RDF particulières qui représentent des règles. Nous proposons une traduction d'un sous-ensemble du langage RIF, le standard pour l'échange de règles sur le Web, en un sous-ensemble du langage SPARQL (les requêtes de la forme CONSTRUCT). Nous utilisons ensuite le langage SPIN pour traduire en RDF ces représentations de règles dans le langage SPARQL. En d'autres termes, pour répondre au problème de la publication et la réutilisation de règles sur le Web, nous l'envisageons comme un problème classique en ingénierie des connaissances de partage et de réutilisation de connaissances. Nous proposons une approche basée sur (1) la représentation en RDF à la fois du contenu et des méta-données associées aux règles, (2) l'interopérabilité de cette représentation avec la recommandation RIF du W3C, (3) leur publication sur le Web de données et (4) la réutilisation de ces règles basée sur l'interrogation de sources de données RDF représentant des règles à l'aide de requêtes SPARQL. L'implémentation et l'évaluation de nos travaux a été réalisée avec le moteur sémantique Corese/KGRAM

LDScript: a Linked Data Script Language

In addition to the existing standards, Web of Data programmers would take advantage of a dedicated programming language enabling them to define functions on RDF terms, triples and graphs as well as SPARQL query results. In particular, this is the case when defining SPARQL extension functions, and the ability to capitalize complex SPARQL filter expressions into extension functions or to define and reuse dedicated aggregates would support modularity and maintenance of the code.Another use case is the definition of functional properties associated to RDF resources and the definition of procedural attachments as functions assigned to RDFS or OWL classes with the selection of the function to be applied to a resource depending on the type of the resource. To address these needs we define a Linked Data Script language on top of the SPARQL filter expression language. We provide the syntax and the semantics of the LDScript language

Publication, partage et réutilisation de règles sur le Web de données

Session 4 : Web sémantiqueNational audienceL'objectif de notre travail présenté dans cet article est de favoriser la réutilisation de règles sur le Web, basée sur les principes du Web de données. En complément de données RDF, de schémas RDFS ou d'ontologies OWL, des règles peuvent être publiées et partagées sur le Web. Notre approche consiste à considérer des bases de règles comme des sources de données, représentées en RDF, qui peuvent être publiées, partagées et interrogées sur le Web de données, permettant ainsi la sélection et la réutilisation des règles pertinentes et utiles dans un contexte ou une application particuliers. Nous envisageons la sélection de règles selon des annotations qui les décrivent ou selon leur contenu, ou les deux. Nous avons implémenté et mis en œuvre notre approche avec le moteur Corese/KGRAM permettant le traitement de données centralisées ou distribuées sur le Web de données et nous avons conduit des expérimentations sur la sélection des règles de la sémantique de OWL pour des données basées sur des ontologies populaires