Ontology Module Extraction via Datalog Reasoning

TL;DR

This paper introduces a Datalog reasoning-based method for ontology module extraction that can produce smaller, more tailored modules by focusing on specific entailments, improving efficiency over existing approaches.

Contribution

The authors present a novel Datalog reasoning approach for ontology module extraction that generalizes existing methods and allows for tailored, smaller modules.

Findings

Approach produces significantly smaller modules in practice.

Method generalizes and improves upon existing approximation techniques.

Evaluation on widely-used ontologies shows encouraging results.

Abstract

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.