解决不一致知识库中缺失事实的可解释补全问题
ABox Abduction for Inconsistent Knowledge Bases under Repair Semantics
- 基于修复语义定义不一致知识库下的断言归因新方法
- 揭示了轻量描述逻辑下归因问题的复杂度全景
- 适合知识库修复与可解释性研究者参考
当知识库（KB）中存在未被蕴含的事实时，断言归因问题旨在寻找可能的扩展以使该事实成立。该问题在诊断、可解释性和修复等场景中有广泛应用。尽管对一致知识库和经典语义下的归因已有充分研究，但对因错误数据导致的不一致知识库情形仍缺乏深入探讨。本文在该设置下定义了合适的归因概念，并提出引导归因生成‘有用’假设的标准。为在不一致环境下恢复有意义推理，采用成熟的修复语义。本文系统分析了轻量描述逻辑DL-Lite和EL_bot下多种变体的断言归因问题的复杂度，构建了完整的复杂度图景。
原文摘要 · Abstract (English)
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.
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