ABox Abduction for Inconsistent Knowledge Bases under Repair Semantics

2026-05-02

AI Summary

This study addresses the problem of generating ABox hypotheses under a repair semantics to explain facts not entailed by an inconsistent knowledge base due to erroneous data. It presents the first systematic investigation of this issue, focusing on lightweight description logics DL-Lite and EL⊥. The work introduces a practical notion of “useful” hypothesis generation and develops corresponding generation models grounded in this criterion. Furthermore, it provides a comprehensive analysis of the computational complexity of the problem across various settings, thereby establishing a theoretical foundation for knowledge base diagnosis and explainability applications.

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.

Problem

• ABox abduction
• inconsistent knowledge bases
• repair semantics
• description logics
• non-entailed fact

Innovation

• ABox abduction
• inconsistent knowledge bases
• repair semantics
• description logics
• computational complexity