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.

翻译：给定一个不蕴含某个事实的知识库（KB），ABox溯因问题旨在寻找该知识库可能的扩展，使其蕴含该事实。该问题具有广泛的应用，涵盖诊断、可解释性及修复等多个领域。针对一致知识库与经典语义下的ABox溯因问题已有深入研究，但对于由错误数据导致的不一致知识库情况，相关研究尚不充分。本文在该场景下定义了合适的溯因概念，并提出了引导溯因产生"有用"假设的准则。为在不一致环境下恢复有意义的推理，我们采用了成熟的修复语义。本文系统性地刻画了基于修复语义的ABox溯因计算复杂度全景图，针对轻量级描述逻辑DL-Lite与EL_bot处理了不同变体的溯因问题。