# Semantics, Ontology and Explanation

arXiv: https://arxiv.org/abs/2304.11124 · HTML: https://mlsanity.com/paper/2304.11124/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 76 references that are in the map.
- First posted on arXiv 21 Apr 2023.

## Builds on (most cited first, 4 of 4)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Semantics and explanation: why counterfactual explanations produce adversarial examples in deep neural networks](https://mlsanity.com/paper/2012.10076/) (2020)
- [Designing Inherently Interpretable Machine Learning Models](https://mlsanity.com/paper/2111.01743/) (2021)

## Built on by (most cited first, 2 of 2)

- [Discerning and Characterising Types of Competency Questions for Ontologies](https://mlsanity.com/paper/2412.13688/) (2024)
- [On the Multiple Roles of Ontologies in Explainable AI](https://mlsanity.com/paper/2311.04778/) (2023)

## Abstract (arXiv)

The terms 'semantics' and 'ontology' are increasingly appearing together with 'explanation', not only in the scientific literature, but also in organizational communication. However, all of these terms are also being significantly overloaded. In this paper, we discuss their strong relation under particular interpretations. Specifically, we discuss a notion of explanation termed ontological unpacking, which aims at explaining symbolic domain descriptions (conceptual models, knowledge graphs, logical specifications) by revealing their ontological commitment in terms of their assumed truthmakers, i.e., the entities in one's ontology that make the propositions in those descriptions true. To illustrate this idea, we employ an ontological theory of relations to explain (by revealing the hidden semantics of) a very simple symbolic model encoded in the standard modeling language UML. We also discuss the essential role played by ontology-driven conceptual models (resulting from this form of explanation processes) in properly supporting semantic interoperability tasks. Finally, we discuss the relation between ontological unpacking and other forms of explanation in philosophy and science, as well as in the area of Artificial Intelligence.