Ontology Module Extraction via Datalog Reasoning


  • Ana Armas Romero University of Oxford
  • Mark Kaminski University of Oxford
  • Bernardo Cuenca Grau University of Oxford
  • Ian Horrocks University of Oxford




ontologies, module extraction, datalog reasoning


Module extraction — the task of computing a (preferably small) fragment M of an ontology T that preserves entailments over a signature S — has found many applications in recent years. Extracting modules of minimal size is, however, computationally hard, and often algorithmically infeasible. Thus, practical techniques are based on approximations, where M provably captures the relevant entailments, but is not guaranteed to be minimal. Existing approximations, however, ensure that M preserves all second-order entailments of T w.r.t. S, which is stronger than is required in many applications, and may lead to large modules in practice. In this paper we propose a novel approach in which module extraction is reduced to a reasoning problem in datalog. Our approach not only generalises existing approximations in an elegant way, but it can also be tailored to preserve only specific kinds of entailments, which allows us to extract significantly smaller modules. An evaluation on widely-used ontologies has shown very encouraging results.




How to Cite

Armas Romero, A., Kaminski, M., Cuenca Grau, B., & Horrocks, I. (2015). Ontology Module Extraction via Datalog Reasoning. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1). https://doi.org/10.1609/aaai.v29i1.9418



AAAI Technical Track: Knowledge Representation and Reasoning