Franck Michel

Translation of Relational and Non-Relational Databases into RDF with xR2RML

With the growing amount of data being continuously produced, it is crucial to come up with solutions to expose data from ever more heterogeneous databases (e.g. NoSQL systems) as linked data. In this paper we present xR2RML, a language designed to describe the mapping of various types of databases to RDF. xR2RML flexibly adapts to heterogeneous query languages and data models while remaining free from any specific language or syntax. It extends R2RML, the W3C recommendation for the mapping of relational databases to RDF, and relies on RML for the handling of various data representation formats. We analyse data models of several modern databases as well as the format in which query results are returned, and we show that xR2RML can translate any data element within such results into RDF, relying on existing languages such as XPath and JSONPath if needed. We illustrate some features of xR2RML such as the generation of RDF collections and containers, and the ability to deal with mixed content.

Grid-wide neuroimaging data federation in the context of the NeuroLOG project.

Grid technologies are appealing to deal with the challenges raised by computational neurosciences and support multi-centric brain studies. However, core grids middleware hardly cope with the complex neuroimaging data representation and multi-layer data federation needs. Moreover, legacy neuroscience environments need to be preserved and cannot be simply superseded by grid services. This paper describes the NeuroLOG platform design and implementation, shedding light on its Data Management Layer. It addresses the integration of brain image files, associated relational metadata and neuroscience semantic data in a heterogeneous distributed environment, integrating legacy data managers through a mediation layer.

A Mapping-Based Method to Query MongoDB Documents with SPARQL

Accessing legacy data as virtual RDF stores is a key issue in the building of the Web of Data. In recent years, the MongoDB database has become a popular actor in the NoSQL market, making it a significant potential contributor to the Web of Linked Data. Therefore, in this paper we address the question of how to access arbitrary MongoDB documents with SPARQL. We propose a two-step method to (i) translate a SPARQL query into a pivot abstract query under MongoDB-to-RDF mappings represented in the xR2RML language, then (ii) translate the pivot query into a concrete MongoDB query. We elaborate on the discrepancy between the expressiveness of SPARQL and the MongoDB query language, and we show that we can always come up with a rewriting that shall produce all correct answers.

A Generic Mapping-based Query Translation from SPARQL to Various Target Database Query Languages

Fostering the development of SPARQL interfaces to heterogeneous databases is a key to efficiently expose legacy data as RDF on the Web. To deal with the variety of modern database formats and query languages, this paper describes a two-step approach to translate a SPARQL query into an equivalent target database query. First, given an xR2RML mapping describing how native database entities can be mapped to RDF, a SPARQL query is translated into a pivot abstract query language independent of the database. In a second step, the pivot query is translated into the target database query language, considering the specific database capabilities. The paper focuses on the first step of the query translation, from SPARQL to a pivot query that takes into account join constraints and SPARQL filters, and embeds conditions entailed by matching SPARQL graph patterns with relevant mappings. It discusses the query optimisations that can be implemented at this level, and briefly describes an application to the case of MongoDB, a NoSQL document store.

Translation of Heterogeneous Databases into RDF, and Application to the Construction of a SKOS Taxonomical Reference

While the data deluge accelerates, most of the data produced remains locked in deep Web databases. For the linked open data to benefit from the potential represented by this huge amount of data, it is crucial to come up with solutions to expose heterogeneous databases as linked data. The xR2RML mapping language is an endeavor towards this goal: it is designed to map various types of databases to RDF, by flexibly adapting to heterogeneous query languages and data models while remaining free from any specific language. It extends R2RML, the W3C recommendation for the mapping of relational databases to RDF, and relies on RML for the handling of various data formats.