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RNTI

MODULAD
Slider : un Raisonneur Incrémental Évolutif
In EGC 2016, vol. RNTI-E-30, pp.537-538
Résumé
The main drawbacks of current reasoning methods over ontologies are they struggle to
provide scalability for large datasets. The batch processing reasoners who provide the best
scalability so far are unable to infer knowledge from evolving data. We contribute to solving
these problems by introducing Slider, an efficient incremental reasoner. Slider exhibits a performance
improvement by more than a 70% compared to the OWLIM-SE reasoner. Slider is
conceived to handle expanding data from streams with a growing background knowledge base.
It natively supports df and RDFS, and its architecture allows to extend it to more complex
fragments with a minimal effort.