Modular Materialisation of Datalog Programs

Authors

  • Pan Hu University of Oxford
  • Boris Motik University of Oxford
  • Ian Horrocks University of Athens

DOI:

https://doi.org/10.1609/aaai.v33i01.33012859

Abstract

The seminaïve algorithm can be used to materialise all consequences of a datalog program, and it also forms the basis for algorithms that incrementally update a materialisation as the input facts change. Certain (combinations of) rules, however, can be handled much more efficiently using custom algorithms. To integrate such algorithms into a general reasoning approach that can handle arbitrary rules, we propose a modular framework for computing and maintaining a materialisation. We split a datalog program into modules that can be handled using specialised algorithms, and we handle the remaining rules using the semina¨ıve algorithm. We also present two algorithms for computing the transitive and the symmetric– transitive closure of a relation that can be used within our framework. Finally, we show empirically that our framework can handle arbitrary datalog programs while outperforming existing approaches, often by orders of magnitude.

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Published

2019-07-17

How to Cite

Hu, P., Motik, B., & Horrocks, I. (2019). Modular Materialisation of Datalog Programs. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 2859-2866. https://doi.org/10.1609/aaai.v33i01.33012859

Issue

Section

AAAI Technical Track: Knowledge Representation and Reasoning