# Predicting the Score of Atomic Candidate OWL Class Axioms

arXiv: https://arxiv.org/abs/2212.10841 · HTML: https://mlsanity.com/paper/2212.10841/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 27 references that are in the map.
- First posted on arXiv 21 Dec 2022.

## Builds on (most cited first, 1 of 1)

- [An evaluative baseline for geo-semantic relatedness and similarity](https://mlsanity.com/paper/1402.3371/) (2014)

## Built on by (most cited first, 1 of 1)

- [Ontology Learning with LLMs: A Benchmark Study on Axiom Identification](https://mlsanity.com/paper/2512.05594/) (2025)

## Abstract (arXiv)

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.