Improving the Competency of First-Order Ontologies

TL;DR

This paper presents a framework for evaluating and enhancing first-order ontologies using automated theorem provers and competency questions, leading to improved versions of SUMO-based ontologies with better reasoning capabilities.

Contribution

It introduces a novel evaluation framework with new competency questions and demonstrates how it improves the reasoning competency of SUMO-based ontologies.

Findings

Adimen-SUMO v2.2 outperforms TPTP-SUMO.

New version Adimen-SUMO v2.4 infers 'Humans can reason'.

Framework effectively guides ontology improvement.

Abstract

We introduce a new framework to evaluate and improve first-order (FO) ontologies using automated theorem provers (ATPs) on the basis of competency questions (CQs). Our framework includes both the adaptation of a methodology for evaluating ontologies to the framework of first-order logic and a new set of non-trivial CQs designed to evaluate FO versions of SUMO, which significantly extends the very small set of CQs proposed in the literature. Most of these new CQs have been automatically generated from a small set of patterns and the mapping of WordNet to SUMO. Applying our framework, we demonstrate that Adimen-SUMO v2.2 outperforms TPTP-SUMO. In addition, using the feedback provided by ATPs we have set an improved version of Adimen-SUMO (v2.4). This new version outperforms the previous ones in terms of competency. For instance, "Humans can reason" is automatically inferred from Adimen-SUMO v2.4, while it is neither deducible from TPTP-SUMO nor Adimen-SUMO v2.2.