Can You Tell the Difference? Contrastive Explanations for ABox Entailments

Authors

  • Patrick Koopmann Knowledge in Artificial Intelligence, Vrije Universiteit Amsterdam, The Netherlands
  • Yasir Mahmood Data Science Group, Heinz Nixdorf Institute, Paderborn University, Germany
  • Axel-Cyrille Ngonga Ngomo Data Science Group, Heinz Nixdorf Institute, Paderborn University, Germany
  • Balram Tiwari Data Science Group, Heinz Nixdorf Institute, Paderborn University, Germany

DOI:

https://doi.org/10.1609/aaai.v40i23.38993

Abstract

We introduce the notion of contrastive ABox explanations to answer questions of the type “Why is a an instance of C, but b is not?”. While there are various approaches for explaining positive entailments (why is C(a) entailed by the knowledge base) as well as missing entailments (why is C(b) not entailed) in isolation, contrastive explanations consider both at the same time, which allows them to focus on the relevant commonalities and differences between a and b. We develop an appropriate notion of contrastive explanations for the special case of ABox reasoning with description logic ontologies, and analyze the computational complexity for different variants under different optimality criteria, considering lightweight as well as more expressive description logics. We implemented a first method for computing one variant of contrastive explanations, and evaluated it on generated problems for realistic knowledge bases.

Published

2026-03-14

How to Cite

Koopmann, P., Mahmood, Y., Ngonga Ngomo, A.-C., & Tiwari, B. (2026). Can You Tell the Difference? Contrastive Explanations for ABox Entailments. Proceedings of the AAAI Conference on Artificial Intelligence, 40(23), 19189-19197. https://doi.org/10.1609/aaai.v40i23.38993

Issue

Section

AAAI Technical Track on Knowledge Representation and Reasoning