Explanations for query answers under existential rules

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
İsmail İlkan Ceylan , Thomas Lukasiewicz , Enrico Malizia , Andrius Vaicenavičius
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引用次数: 0

Abstract

Ontology-based data access is an extensively studied paradigm aiming at improving query answers with the use of an “ontology”. An ontology is a specification of a domain of interest, which, in this context, is described via a logical theory. As a form of logical entailment, ontology-mediated query answering is fully interpretable, which makes it possible to derive explanations for ontological query answers. This is a quite important aspect, as the fact that many recent AI systems mostly operating as black boxes has led to some serious concerns. In the literature, various works on explanations in the context of description logics (DLs) have appeared, mostly focusing on explaining concept subsumption and concept unsatisfiability in the ontologies. Some works on explaining query entailment in DLs have appeared as well, however, mainly dealing with inconsistency-tolerant semantics and, actually, non-entailment of the queries. Surprisingly, explaining ontological query entailment has received little attention for ontology languages based on existential rules. In fact, although DLs are popular formalisms to model ontologies, it is generally agreed that rule-based ontologies are well-suited for data-intensive applications, as they allow us to conveniently deal with higher-arity relations, which naturally occur in standard relational databases. The goal of this work is to close this gap, and study the problem of explaining query entailment in the context of existential rules ontologies in terms of minimal subsets of database facts. We provide a thorough complexity analysis for several decision problems associated with minimal explanations for various classes of existential rules, and for different complexity measures.
存在规则下查询答案的解释
基于本体的数据访问是一种被广泛研究的范式,旨在通过使用“本体”来改进查询答案。本体是感兴趣的领域的规范,在这种情况下,它是通过逻辑理论来描述的。作为逻辑蕴涵的一种形式,本体中介查询回答是完全可解释的,这使得推导本体查询答案的解释成为可能。这是一个非常重要的方面,因为许多最近的AI系统大多以黑盒子的方式运行,这导致了一些严重的担忧。在文献中,出现了各种描述逻辑背景下的解释工作,主要集中在解释本体中的概念包容和概念不满足性。一些解释dl中的查询蕴涵的工作也出现了,然而,主要是处理不一致容忍语义,实际上是查询的非蕴涵。令人惊讶的是,对于基于存在规则的本体语言,解释本体查询蕴涵却很少受到关注。事实上,尽管dl是为本体建模的流行形式化方法,但人们普遍认为,基于规则的本体非常适合于数据密集型应用程序,因为它们允许我们方便地处理在标准关系数据库中自然出现的更高密度的关系。这项工作的目标是缩小这一差距,并研究在存在规则本体的背景下,根据数据库事实的最小子集解释查询蕴涵的问题。我们为几个决策问题提供了全面的复杂性分析,这些决策问题与各种存在规则的最小解释和不同的复杂性度量相关。
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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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