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Rethinking Explanations: Formalizing Contrast in Description Logics

arXiv 2605.01442 ↗First posted 2 May 2026cs.AI math.LOcited by 0

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Abstract (arXiv)

There has been a growing interest in explaining entailments over description logic (DL) knowledge bases. The existing explanation formalisms focus on justifications to explain true axioms, and abductive reasoning to explain missing axioms in a knowledge base. However, these formalisms only point out the reasoning steps behind a (missing) entailment and lack a user-centered approach as they do not consider an inquirer's needs, level of understanding, or prior knowledge. We propose contrastive explanations, aiming at answering "why an axiom P (fact) is true instead of another axiom Q (foil)" over description logic knowledge bases. The motivation arises from the observation that when a user discovers that P has occurred, they are often surprised because they anticipated the occurrence of another similar event Q. Furthermore, individual explanations for "why P" and "why not Q" are unsatisfactory since a user expects to see the difference between P and Q. In this work, we first present formal foundations of contrasting questions and then define contrastive explanations within description logics. To this end, facts include ABox assertions of the form C(x) for a concept C and individual x. Possible foils for such facts are assertions C(y) (contrasting against an individual y), or D(x) (contrasting against a concept D). Additionally, we explore the properties of contrastive explanations in the DL EL and ALC. We also provide an implementation of our definition and an experimental evaluation on KBs of varying sizes.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 49 references that are in the map. First posted on arXiv 2 May 2026.

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References · 10

  1. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  2. 2018Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negativescited by 261
  3. 2018Contrastive Explanation: A Structural-Model Approachcited by 64
  4. 2021Signature-Based Abduction with Fresh Individuals and Complex Concepts for Description Logics (Extended Version)cited by 5
  5. 2020Efficient computation of contrastive explanationscited by 4
  6. 2021Contrastive Explanations for Argumentation-Based Conclusionscited by 4
  7. 2023Efficient Computation of General Modules for ALC Ontologies (Extended Version)cited by 4
  8. 2025Why this and not that? A Logic-based Framework for Contrastive Explanationscited by 3
  9. 2025Why not? Developing ABox Abduction beyond Repairscited by 2
  10. 2025Can You Tell the Difference? Contrastive Explanations for ABox Entailmentscited by 1

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  1. 2023Formal Proofs as Structured Explanations: Proposing Several Tasks on Explainable Natural Language Inference
  2. 2022Evonne: Interactive Proof Visualization for Description Logics (System Description) -- Extended Version
  3. 2024Disentangling Logic: The Role of Context in Large Language Model Reasoning Capabilities
  4. 2022Explanatory Paradigms in Neural Networks
  5. 2026Axiomatic Foundations of Counterfactual Explanations
  6. 2024Faithfulness vs. Plausibility: On the (Un)Reliability of Explanations from Large Language Models
  7. 2025Analysing Temporal Reasoning in Description Logics Using Formal Grammars
  8. 2024Eliciting Critical Reasoning in Retrieval-Augmented Language Models via Contrastive Explanations