发表机构
Université Clermont Auvergne, LIMOS, CNRS France（克莱蒙特大学，LIMOS，CNRS法国）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究混合MKNF知识库在规则组件中不支持经典否定的问题，提出扩展混合MKNF以支持经典否定，定义其语法和语义并给出计算有充分根据模型的过程，增强了表示显式否定知识的能力。
AI 中文摘要
基于有充分根据语义的混合MKNF知识库将描述逻辑与逻辑编程相结合。然而，它们在规则组件中不支持经典否定，限制了表示显式否定知识的能力。在安全关键应用中，这种限制尤为显著。为解决此问题，我们引入了一种扩展的混合MKNF，它在规则组件中支持经典否定。我们正式定义了扩展语言的语法和语义，并给出了计算其有充分根据模型的一般过程。
英文摘要
Hybrid MKNF knowledge bases under the well-founded semantics integrate Description Logics with Logic Programming. However, they do not support classical negation in the rule component, limiting their ability to represent explicit negative knowledge. This limitation is particularly significant in safety-critical applications, where reasoning often requires explicit negative information rather than interpreting the absence of information as evidence of absence. To address this issue, we introduce an extension of Hybrid MKNF that supports classical negation in the rule component. We formally define the syntax and semantics of the extended language and present a general procedure for computing its well-founded model.
CommentsIn Proceedings ICLP 2026, arXiv:2607.17707
Journal refEPTCS 450, 2026, pp. 374-402
