KR2026Proceedings of the 23rd International Conference on Principles of Knowledge Representation and ReasoningProceedings of the 23rd International Conference on Principles of Knowledge Representation and Reasoning

Lisbon, Portugal. July 20-23, 2026.

Edited by
  1. Renata Wassermann
  2. Marie-Laure Mugnier
  3. Franz Baader

ISSN: 2334-1033
ISBN: 978-1-956792-18-8

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Copyright © 2026 International Joint Conferences on Artificial Intelligence Organization

ABox Abduction for Inconsistent Knowledge Bases under Repair Semantics

  1. Anselm Haak(Knowledge Representation Group, Paderborn University)
  2. Patrick Koopmann(Knowledge in Artificial Intelligence, Vrije Universiteit Amsterdam)
  3. Yasir Mahmood(Data Science Group, Heinz Nixdorf Institute, Paderborn University)
  4. Anni-Yasmin Turhan(Knowledge Representation Group, Paderborn University)
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BibTeX
https://doi.org/10.24963/kr.2026/37

Keywords

  1. null-abduction
  2. null-Repair semantics
  3. null-Inconsistency-tolerant reasoning

Abstract

Given a knowledge base (KB) with a non-entailed fact, the ABox abduction problem asks for possible extensions of the KB that would entail this fact. This problem has many applications, ranging from diagnosis to explainability and repair. ABox abduction has been well-investigated for consistent KBs and classical semantics, but little is known for the case of inconsistent KBs, which can be caused by erroneous data.

In this paper we define suitable notions of abduction in this setting and propose criteria that guide abduction towards useful hypotheses. To regain meaningful reasoning in the presence of inconsistencies, we use well-established repair semantics.

We provide a comprehensive landscape of the complexity of ABox abduction under repair semantics, treating different variants of the abduction problem for the light-weight description logics DL-Lite and EL_bot.