How to tractably compute a productive repair for possibilistic partially ordered DL-LiteR ontologies?

IF 3.2 1区 数学 Q2 COMPUTER SCIENCE, THEORY & METHODS
Ahmed Laouar , Sihem Belabbes , Salem Benferhat
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引用次数: 0

Abstract

The lightweight description logic dialect DL-LiteR offers a framework for specifying and reasoning with formal inconsistent ontologies. Basically, an ontology is a knowledge base composed of a TBox, modelling conceptual knowledge of some domain of interest, and an ABox, asserting factual knowledge about specific entities of the domain. Inconsistency in an ontology is usually handled by evaluating queries over maximal conflict-free subsets of the ABox, called data repairs. Several inconsistency-tolerant semantics, with different levels of cautiousness and computational cost, propose strategies for selecting the repairs to consider when deriving new conclusions from an inconsistent ontology. In this paper, we focus on partially ordered ontologies where a partial order relation captures the reliability levels of the ABox elements. We propose a new tractable method, called “Cπ-repair”, which leverages possibility theory in repairing a partially ordered ABox. It proceeds in four steps as follows. First, the partial order relation is extended into a family of total orders, thus inducing as many compatible totally ordered ABoxes. Second, a single repair is computed for each compatible ABox. Third, these repairs are closed deductively in order to improve their productivity, i.e., to derive more facts. Finally, the closed repairs are intersected to produce a single repair for the initial partially ordered ABox. The main contribution of this paper is an equivalent characterization that determines the validity of the conclusions drawn with the “Cπ-repair” method, but without eliciting the compatible ABoxes or computing their repairs. This allows us to establish the tractability of the method by reformulating the problem using the notions of support for an assertion and dominance over the conflicts that arise between the ABox elements. Essentially, the valid conclusions are those derived from the supports that dominate all conflicts. In the last part of the paper, we explore the rationality properties of our method. We show that increasing repair productivity does not alter the satisfaction of the rationality properties. We also discuss the applicability of our proposed method to languages richer than DL-LiteR and to other inconsistency-tolerant semantics.
如何可跟踪地计算可能部分有序dl - l本体的生产性修复?
轻量级描述逻辑方言dl - l提供了一个框架,用于对形式不一致的本体进行指定和推理。基本上,本体是一个知识库,它由一个TBox和一个ABox组成,前者为感兴趣的领域的概念性知识建模,后者断言关于该领域特定实体的事实性知识。本体中的不一致性通常通过评估对ABox的最大无冲突子集的查询来处理,称为数据修复。几种具有不同谨慎程度和计算成本的不一致容忍语义提出了从不一致本体中得出新结论时选择要考虑的修复的策略。在本文中,我们将重点放在偏序本体上,其中偏序关系捕获ABox元素的可靠性级别。我们提出了一种新的易于处理的方法,称为“π-修复”,利用可能性理论修复部分有序的ABox。它分以下四个步骤进行。首先,将偏序关系推广到全序族中,从而归纳出尽可能多的兼容全序box。其次,为每个兼容ABox计算单个修复。第三,这些修复是演绎封闭的,以提高它们的生产率,即推导出更多的事实。最后,闭合修复被交叉以产生初始部分有序ABox的单个修复。本文的主要贡献是等效表征,确定了用“π-修复”方法得出的结论的有效性,但没有引出相容abox或计算其修复。这允许我们通过使用支持断言和支配ABox元素之间产生的冲突的概念来重新表述问题,从而建立方法的可跟踪性。从本质上讲,有效的结论是从主导所有冲突的支持中得出的。在论文的最后一部分,我们探讨了该方法的合理性。结果表明,提高维修生产率并不会改变对合理性的满意程度。我们还讨论了我们提出的方法对比dl - l更丰富的语言和其他不一致容忍语义的适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Fuzzy Sets and Systems
Fuzzy Sets and Systems 数学-计算机:理论方法
CiteScore
6.50
自引率
17.90%
发文量
321
审稿时长
6.1 months
期刊介绍: Since its launching in 1978, the journal Fuzzy Sets and Systems has been devoted to the international advancement of the theory and application of fuzzy sets and systems. The theory of fuzzy sets now encompasses a well organized corpus of basic notions including (and not restricted to) aggregation operations, a generalized theory of relations, specific measures of information content, a calculus of fuzzy numbers. Fuzzy sets are also the cornerstone of a non-additive uncertainty theory, namely possibility theory, and of a versatile tool for both linguistic and numerical modeling: fuzzy rule-based systems. Numerous works now combine fuzzy concepts with other scientific disciplines as well as modern technologies. In mathematics fuzzy sets have triggered new research topics in connection with category theory, topology, algebra, analysis. Fuzzy sets are also part of a recent trend in the study of generalized measures and integrals, and are combined with statistical methods. Furthermore, fuzzy sets have strong logical underpinnings in the tradition of many-valued logics.
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