# Explaining ELH Concept Descriptions through Counterfactual Reasoning

arXiv: https://arxiv.org/abs/2301.05109 · HTML: https://mlsanity.com/paper/2301.05109/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 56 references that are in the map.
- First posted on arXiv 12 Jan 2023.

## Builds on (most cited first, 16 of 16)

- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [FACE: Feasible and Actionable Counterfactual Explanations](https://mlsanity.com/paper/1909.09369/) (2019)
- [Multi-Objective Counterfactual Explanations](https://mlsanity.com/paper/2004.11165/) (2020)
- [Efficient Concept Induction for Description Logics](https://mlsanity.com/paper/1812.03243/) (2018)
- [EvoLearner: Learning Description Logics with Evolutionary Algorithms](https://mlsanity.com/paper/2111.04879/) (2021)
- [Predicting Completeness in Knowledge Bases](https://mlsanity.com/paper/1612.05786/) (2016)
- [Searching News Articles Using an Event Knowledge Graph Leveraged by Wikidata](https://mlsanity.com/paper/1904.05557/) (2019)
- [Learning Concept Lengths Accelerates Concept Learning in ALC](https://mlsanity.com/paper/2107.04911/) (2021)
- [Neural Class Expression Synthesis](https://mlsanity.com/paper/2111.08486/) (2021)
- [Signature-Based Abduction with Fresh Individuals and Complex Concepts for Description Logics (Extended Version)](https://mlsanity.com/paper/2105.00274/) (2021)
- [Correcting Knowledge Base Assertions](https://mlsanity.com/paper/2001.06917/) (2020)
- [DRILL-- Deep Reinforcement Learning for Refinement Operators in ALC](https://mlsanity.com/paper/2106.15373/) (2021)
- [Rows from Many Sources: Enriching row completions from Wikidata with a pre-trained Language Model](https://mlsanity.com/paper/2204.07014/) (2022)

## Abstract (arXiv)

Knowledge bases are widely used for information management, enabling high-impact applications such as web search, question answering, and natural language processing. They also serve as the backbone for automatic decision systems, e.g., for medical diagnostics and credit scoring. As stakeholders affected by these decisions would like to understand their situation and verify how fair the decisions are, a number of explanation approaches have been proposed. An intrinsically transparent way to do classification is by using concepts in description logics. However, these concepts can become long and difficult to fathom for non-experts, even when verbalized. One solution is to employ counterfactuals to answer the question, ``How must feature values be changed to obtain a different classification?'' By focusing on the minimal feature changes, the explanations are short, human-friendly, and provide a clear path of action regarding the change in prediction. While previous work investigated counterfactuals for tabular data, in this paper, we transfer the notion of counterfactuals to knowledge bases and the description logic ELH. Our approach starts by generating counterfactual candidates from concepts, followed by selecting the candidates requiring the fewest feature changes as counterfactuals. When multiple counterfactuals exist, we rank them based on the likeliness of their feature combinations. We evaluate our method by conducting a user survey to determine which counterfactual candidates participants prefer for explanation.