关于利用规则路径约束测量图数据库中的不一致性

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Artificial Intelligence Pub Date : 2024-08-02 DOI:10.1016/j.artint.2024.104197
John Grant , Francesco Parisi
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引用次数: 0

摘要

现实世界中的数据往往是不一致的。虽然已有大量关于测量不一致性的研究,但这些研究主要集中在以命题逻辑形式化的知识库上。最近,关系数据库也引入了不一致性测量方法。然而,如今现实世界中的信息总是更多地以基于图的结构来表示,这种结构比关系型结构提供了更直观的概念化。在本文中,我们将探讨具有规则路径约束的图数据库的不一致性度量,规则路径约束是一类基于著名的图数据导航语言的完整性约束。在这种情况下,我们定义了几种不一致度量方法,用于处理导致图数据库不一致的特定因素。我们还定义了一些合理性假设,这些假设是图数据库不一致性度量的理想属性。我们分析了每种度量方法是否符合每个假设,并发现了不同程度的满足情况;事实上,其中一种度量方法满足了所有假设。最后,我们研究了计算所有度量的数据和综合复杂性,以及判定度量是否小于、等于或大于给定阈值的复杂性。结果表明,对于大多数度量,这些问题都是可以解决的,而对于其他度量,则表现出不同程度的难解性。
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On measuring inconsistency in graph databases with regular path constraints

Real-world data are often inconsistent. Although a substantial amount of research has been done on measuring inconsistency, this research concentrated on knowledge bases formalized in propositional logic. Recently, inconsistency measures have been introduced for relational databases. However, nowadays, real-world information is always more frequently represented by graph-based structures which offer a more intuitive conceptualization than relational ones. In this paper, we explore inconsistency measures for graph databases with regular path constraints, a class of integrity constraints based on a well-known navigational language for graph data. In this context, we define several inconsistency measures dealing with specific elements contributing to inconsistency in graph databases. We also define some rationality postulates that are desirable properties for an inconsistency measure for graph databases. We analyze the compliance of each measure with each postulate and find various degrees of satisfaction; in fact, one of the measures satisfies all the postulates. Finally, we investigate the data and combined complexity of the calculation of all the measures as well as the complexity of deciding whether a measure is lower than, equal to, or greater than a given threshold. It turns out that for a majority of the measures these problems are tractable, while for the other different levels of intractability are exhibited.

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来源期刊
Artificial Intelligence
Artificial Intelligence 工程技术-计算机:人工智能
CiteScore
11.20
自引率
1.40%
发文量
118
审稿时长
8 months
期刊介绍: The Journal of Artificial Intelligence (AIJ) welcomes papers covering a broad spectrum of AI topics, including cognition, automated reasoning, computer vision, machine learning, and more. Papers should demonstrate advancements in AI and propose innovative approaches to AI problems. Additionally, the journal accepts papers describing AI applications, focusing on how new methods enhance performance rather than reiterating conventional approaches. In addition to regular papers, AIJ also accepts Research Notes, Research Field Reviews, Position Papers, Book Reviews, and summary papers on AI challenges and competitions.
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