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Section: Research Program
Scalable and Expressive Techniques for the Semantic Web
The Semantic Web vision of a world-wide interconnected database of facts, describing resources by means of semantics, is coming within reach as the W3C's RDF (Resource Description Format) data model is gaining traction. The W3C Linking Open Data initiative has boosted the publication and interlinkage of a large number of datasets on the semantic web resulting to the Linked Open Data Cloud. These datasets of billions of RDF triples have been created and published online. Moreover, numerous datasets and vocabularies from different application domains are published nowadays as RDF graphs in order to facilitate community annotation and interlinkage of both scientific and scholarly data of interest. RDF storage, querying, and reasoning is now supported by a host of tools whose scalability and expressive power vary widely. Unsurprisingly, some of the most scalable tools draw upon the existing models and architecture for managing structured data. However, such tools often ignore the semantic aspects that make RDF interesting. For what concerns the semantics, a delicate balance must be found between expressive power and the efficiency of the resulting data management algorithms.
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The team works on identifying tractable dialects of RDF, amenable to highly efficient query answering algorithms, taking into account both data and semantics.
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Another line of research investigates the usage of RDF data and semantics to help structure, organize, and enrich other kinds of data, and in particular structured documents. The newly started DigiCosme LabEx grant “Structured, Social and Semantic Search” is part of this research.
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Last but not least, we investigate novel models and algorithms for efficient Semantic Web data management, going beyond the existing standard languages. In particular, we study formal, flexible models for an all-RDF data analytics framework, combining the rich structure and semantics of RDF with the power of analysis tools previously developed for relational data, such as analytical schemas and queries. This work is related to our Digiteo grant “Data Warehouses for RDF” (DW4RDF) and will continue as part of our recently started “Investissement d'Avenir” project Datalyse.