Reasoning on Multirelational Contextual Hierarchies via Answer Set Programming with Algebraic Measures

IF 1.4 2区 数学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Theory and Practice of Logic Programming Pub Date : 2021-08-06 DOI:10.1017/S1471068421000284
Loris Bozzato, Thomas Eiter, Rafael Kiesel
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引用次数: 9

Abstract

Abstract Dealing with context-dependent knowledge has led to different formalizations of the notion of context. Among them is the Contextualized Knowledge Repository (CKR) framework, which is rooted in description logics but links on the reasoning side strongly to logic programs and Answer Set Programming (ASP) in particular. The CKR framework caters for reasoning with defeasible axioms and exceptions in contexts, which was extended to knowledge inheritance across contexts in a coverage (specificity) hierarchy. However, the approach supports only this single type of contextual relation and the reasoning procedures work only for restricted hierarchies, due to nontrivial issues with model preference under exceptions. In this paper, we overcome these limitations and present a generalization of CKR hierarchies to multiple contextual relations, along with their interpretation of defeasible axioms and preference. To support reasoning, we use ASP with algebraic measures, which is a recent extension of ASP with weighted formulas over semirings that allows one to associate quantities with interpretations depending on the truth values of propositional atoms. Notably, we show that for a relevant fragment of CKR hierarchies with multiple contextual relations, query answering can be realized with the popular asprin framework. The algebraic measures approach is more powerful and enables, for example, reasoning with epistemic queries over CKRs, which opens interesting perspectives for the use of quantitative ASP extensions in other applications.
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基于代数测度的回答集规划的多关系上下文层次推理
对上下文相关知识的处理导致了上下文概念的不同形式化。其中包括情境化知识库(CKR)框架,它植根于描述逻辑,但在推理方面与逻辑程序和答案集编程(ASP)紧密相连。CKR框架迎合了在上下文中使用可否定的公理和例外的推理,它被扩展到覆盖(特异性)层次结构中跨上下文的知识继承。然而,该方法只支持这种单一类型的上下文关系,并且由于例外情况下模型偏好的重要问题,推理过程仅适用于受限制的层次结构。在本文中,我们克服了这些限制,并提出了CKR层次结构对多个上下文关系的推广,以及它们对可否定公理和偏好的解释。为了支持推理,我们使用带有代数度量的ASP,这是ASP在半环上的加权公式的最新扩展,它允许人们将数量与依赖于命题原子的真值的解释联系起来。值得注意的是,我们表明,对于具有多个上下文关系的CKR层次结构的相关片段,可以使用流行的asprin框架实现查询应答。代数度量方法更强大,例如,支持对ckr进行认知查询的推理,这为在其他应用程序中使用定量ASP扩展开辟了有趣的前景。
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来源期刊
Theory and Practice of Logic Programming
Theory and Practice of Logic Programming 工程技术-计算机:理论方法
CiteScore
4.50
自引率
21.40%
发文量
40
审稿时长
>12 weeks
期刊介绍: Theory and Practice of Logic Programming emphasises both the theory and practice of logic programming. Logic programming applies to all areas of artificial intelligence and computer science and is fundamental to them. Among the topics covered are AI applications that use logic programming, logic programming methodologies, specification, analysis and verification of systems, inductive logic programming, multi-relational data mining, natural language processing, knowledge representation, non-monotonic reasoning, semantic web reasoning, databases, implementations and architectures and constraint logic programming.
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