Predicting the Score of Atomic Candidate OWL Class Axioms

TL;DR

This paper presents a fast, predictive model for scoring candidate OWL class axioms using semantic similarity, enabling efficient ontology validation and induction.

Contribution

It introduces a semantic similarity-based approach to predict axiom scores, reducing computational costs compared to traditional reasoning methods.

Findings

The model accurately predicts the possibility scores of candidate axioms.

It works effectively across various OWL class axioms.

The approach is suitable for iterative search and large-scale ontology tasks.

Abstract

Candidate axiom scoring is the task of assessing the acceptability of a candidate axiom against the evidence provided by known facts or data. The ability to score candidate axioms reliably is required for automated schema or ontology induction, but it can also be valuable for ontology and/or knowledge graph validation. Accurate axiom scoring heuristics are often computationally expensive, which is an issue if you wish to use them in iterative search techniques like level-wise generate-and-test or evolutionary algorithms, which require scoring a large number of candidate axioms. We address the problem of developing a predictive model as a substitute for reasoning that predicts the possibility score of candidate class axioms and is quick enough to be employed in such situations. We use a semantic similarity measure taken from an ontology's subsumption structure for this purpose. We show that the approach provided in this work can accurately learn the possibility scores of candidate OWL class axioms and that it can do so for a variety of OWL class axioms.