# On the Multiple Roles of Ontologies in Explainable AI

arXiv: https://arxiv.org/abs/2311.04778 · HTML: https://mlsanity.com/paper/2311.04778/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 72 references that are in the map.
- First posted on arXiv 8 Nov 2023.

## Builds on (most cited first, 19 of 19)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Explain Yourself! Leveraging Language Models for Commonsense Reasoning](https://mlsanity.com/paper/1906.02361/) (2019)
- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI](https://mlsanity.com/paper/2201.08164/) (2022)
- [Neurosymbolic AI: The 3rd Wave](https://mlsanity.com/paper/2012.05876/) (2020)
- [Logic Tensor Networks](https://mlsanity.com/paper/2012.13635/) (2020)
- [Explaining Explanations in AI](https://mlsanity.com/paper/1811.01439/) (2018)
- [Logical Neural Networks](https://mlsanity.com/paper/2006.13155/) (2020)
- [Neural Network Attributions: A Causal Perspective](https://mlsanity.com/paper/1902.02302/) (2019)
- [What Does Explainable AI Really Mean? A New Conceptualization of Perspectives](https://mlsanity.com/paper/1710.00794/) (2017)
- [Measuring the Quality of Explanations: The System Causability Scale (SCS). Comparing Human and Machine Explanations](https://mlsanity.com/paper/1912.09024/) (2019)
- [GLocalX -- From Local to Global Explanations of Black Box AI Models](https://mlsanity.com/paper/2101.07685/) (2021)
- [A systematic review and taxonomy of explanations in decision support and recommender systems](https://mlsanity.com/paper/2006.08672/) (2020)
- [Directions for Explainable Knowledge-Enabled Systems](https://mlsanity.com/paper/2003.07523/) (2020)
- [A Critical Review of Inductive Logic Programming Techniques for Explainable AI](https://mlsanity.com/paper/2112.15319/) (2021)
- [Foundations of Explainable Knowledge-Enabled Systems](https://mlsanity.com/paper/2003.07520/) (2020)
- [Semantics, Ontology and Explanation](https://mlsanity.com/paper/2304.11124/) (2023)

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

- [A three-Level Framework for LLM-Enhanced eXplainable AI: From technical explanations to natural language](https://mlsanity.com/paper/2506.05887/) (2025)
- [Semantic Web and Software Agents -- A Forgotten Wave of Artificial Intelligence?](https://mlsanity.com/paper/2503.20793/) (2025)

## Abstract (arXiv)

This paper discusses the different roles that explicit knowledge, in particular ontologies, can play in Explainable AI and in the development of human-centric explainable systems and intelligible explanations. We consider three main perspectives in which ontologies can contribute significantly, namely reference modelling, common-sense reasoning, and knowledge refinement and complexity management. We overview some of the existing approaches in the literature, and we position them according to these three proposed perspectives. The paper concludes by discussing what challenges still need to be addressed to enable ontology-based approaches to explanation and to evaluate their human-understandability and effectiveness.