Extracting Bounded-Level Modules from Deductive RDF Triplestores

Authors

  • Marie-Christine Rousset University of Grenoble Alpes
  • Federico Ulliana LIRMM, Montpellier University

DOI:

https://doi.org/10.1609/aaai.v29i1.9176

Keywords:

RDF, datalog, Semantic Web

Abstract

We present a novel semantics for extracting bounded-level modules from RDF ontologies and databases augmented with safe inference rules, a la Datalog. Dealing with a recursive rule language poses challenging issues for defining the module semantics, and also makes module extraction algorithmically unsolvable in some cases. Our results include a set of module extraction algorithms compliant with the novel semantics. Experimental results show that the resulting framework is effective in extracting expressive modules from RDF datasets with formal guarantees, whilst controlling their succinctness.

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Published

2015-02-09

How to Cite

Rousset, M.-C., & Ulliana, F. (2015). Extracting Bounded-Level Modules from Deductive RDF Triplestores. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1). https://doi.org/10.1609/aaai.v29i1.9176