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Abstract

Sequoia is a state-of-the-art system developed by researchers in our department for automated reasoning in OWL 2, a standard ontology language in the Semantic Web. Sequoia uses consequence-based calculus, a novel and promising approach towards fast and scalable reasoning. Consequence-based algorithms are naturally amenable to parallel reasoning; however, the current version of Sequoia has limited support for parallel reasoning. This project aims at optimising Sequoia by identifying new and effective ways to exploit parallel reasoning. The student will perform an extensive empirical evaluation of the current version of Sequoia, identify key bottlenecks, and devise new ways to make efficient and powerful use of parallel reasoning. The student will evaluate the performance of any new versions of the system that they propose, with an eye to understanding their strengths and weaknesses. The student involved in this project will acquire intensive knowledge of state-of-the-art techniques for reasoning in OWL 2.

Pre-requisites: Knowledge Representation and Reasoning course. Experience with concurrent programming and/or Scala is desirable