让知识库能直接表达否定信息，提升安全场景下的推理能力
Hybrid MKNF with Classical Negation in the Rule Component
- 扩展混合MKNF框架，支持规则中使用经典否定
- 提出新语义定义与完备的模型计算方法
- 适合需要明确否定知识的安全关键系统
在基于稳固语义的混合MKNF知识库中，描述逻辑与逻辑编程被整合，但其规则部分不支持经典否定，限制了对显式负知识的表示能力。这一缺陷在安全关键应用中尤为显著，因为此类场景的推理常需显式否定信息，而非通过信息缺失来推断不存在。为解决此问题，本文提出一种扩展的混合MKNF框架，支持规则组件中的经典否定。我们形式化定义了扩展语言的语法与语义，并提出一种通用的算法以计算其稳固模型。
原文摘要 · Abstract (English)
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.
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