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

International audienceWith 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

Parallel RDF generation from heterogeneous big data

To unlock the value of increasingly available data in high volumes, we need flexible ways to integrate data across different sources. While semantic integration can be provided through RDF generation, current generators insufficiently scale in terms of volume. Generators are limited by memory constraints. Therefore, we developed the RMLStreamer, a generator that parallelizes the ingestion and mapping tasks of RDF generation across multiple instances. In this paper, we analyze what aspects are parallelizable and we introduce an approach for parallel RDF generation. We describe how we implemented our proposed approach, in the frame of the RMLStreamer, and how the resulting scaling behavior compares to other RDF generators. The RMLStreamer ingests data at 50% faster rate than existing generators through parallel ingestion

Extending R2RML-F to support dynamic datatype and language tags

Linked data is often generated from raw data with the help of mapping languages. Complex data transformation is one of the essential parts while uplifting data which either can be implemented as custom solutions or separated from the mapping process. In this paper, we propose an approach of separating complex data transformations from the mapping process that can still be reusable across the systems. In the proposed method, complex data transformations include the entailment of (i) language tag and (ii) datatype present at the data source. The proposed method also includes inferring missing datatype information. We extended R2RML-F to handle data transformations. The results showed that transformation functions could be used to create typed literals dynamically. Our approach is validated on the test cases specified by the RDF mapping language (RML). The proposed method considers data in the form of JSON, thus making the system interoperable and reusable

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

International audienceFostering 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

Integration of Web APIs and Linked Data Using SPARQL Micro-Services - Application to Biodiversity Use Cases

International audienceIn recent years, Web APIs have become a de facto standard for exchanging machine-readable data on the Web. Despite this success, however, they often fail in making resource descriptions interoperable due to the fact that they rely on proprietary vocabularies that lack formal semantics.The Linked Data principles similarly seek the massive publication of data on the Web, yet with the specific goal of ensuring semantic interoperability.Given their complementary goals, it is commonly admitted that cross-fertilization could stem from the automatic combination of Linked Data and Web APIs. Towards this goal, in this paper we leverage the micro-service architectural principles to define a SPARQL Micro-Service architecture, aimed at querying Web APIs using SPARQL. A SPARQL micro-service is a lightweight SPARQL endpoint that provides access to a small, resource-centric, virtual graph. In this context, we argue that full SPARQL Query expressiveness can be supported efficiently without jeopardizing servers availability.Furthermore, we demonstrate how this architecture can be used to dynamically assign dereferenceable URIs to Web API resources that do not have URIs beforehand, thus literally “bringing” Web APIs into the Web of Data. We believe that the emergence of an ecosystem of SPARQL micro-services published by independent providers would enable Linked Data-based applications to easily glean pieces of data from a wealth of distributed, scalable, and reliable services. We describe a working prototype implementation and we finally illustrate the use of SPARQL micro-services in the context of two real-life use cases related to the biodiversity domain, developed in collaboration with the French National Museum of Natural History

Mapping-based SPARQL access to a MongoDB database

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 leader in the NoSQL market and the management of very large datasets, making it a significant potential contributor to the Web of Linked Data. Therefore, in this paper we address the research 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 certain answers