Improving the Competency of First-Order Ontologies
This work addresses the challenge of enhancing the competency of first-order ontologies for knowledge representation and reasoning systems, though it is incremental as it builds on existing methodologies and datasets.
The authors tackled the problem of evaluating and improving first-order ontologies by introducing a framework using automated theorem provers and competency questions, resulting in an improved version of Adimen-SUMO (v2.4) that outperforms previous versions, such as automatically inferring 'Humans can reason' where others could not.
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.