Evidence of large-scale conceptual disarray in multi-level taxonomies in Wikidata

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Semantic Web Pub Date : 2024-03-07 DOI:10.3233/sw-243562
Atílio A. Dadalto, João Paulo A. Almeida, Claudenir M. Fonseca, Giancarlo Guizzardi
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Abstract

The distinction between types and individuals is key to most conceptual modeling techniques and knowledge representation languages. Despite that, there are a number of situations in which modelers navigate this distinction inadequately, leading to problematic models. We show evidence of a large number of representation mistakes associated with the failure to employ this distinction in the Wikidata knowledge graph, which can be identified with the incorrect use of instantiation, which is a relation between an instance and a type, and specialization (or subtyping), which is a relation between two types. The prevalence of the problems in Wikidata’s taxonomies suggests that methodological and computational tools are required to mitigate the issues identified, which occur in many settings when individuals, types, and their metatypes are included in the domain of interest. We conduct a conceptual analysis of entities involved in recurrent erroneous cases identified in this empirical data, and present a tool that supports users in identifying some of these mistakes.
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维基数据多级分类法中大规模概念混乱的证据
类型和个体之间的区别是大多数概念建模技术和知识表示语言的关键。尽管如此,在很多情况下,建模者对这种区分的把握并不恰当,从而导致模型出现问题。我们在维基数据知识图谱中展示了大量与未使用这种区分有关的表示错误,这些错误可以通过实例化(实例与类型之间的关系)和特化(或子类型化)的错误使用来识别,实例化是两个类型之间的关系。维基数据分类法中普遍存在的问题表明,需要使用方法学和计算工具来减少已发现的问题,这些问题在许多情况下都会出现,即当个体、类型及其元类被纳入相关领域时。我们对这些经验数据中发现的经常出现的错误案例中涉及的实体进行了概念分析,并提出了一种可帮助用户识别其中一些错误的工具。
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来源期刊
Semantic Web
Semantic Web COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
8.30
自引率
6.70%
发文量
68
期刊介绍: The journal Semantic Web – Interoperability, Usability, Applicability brings together researchers from various fields which share the vision and need for more effective and meaningful ways to share information across agents and services on the future internet and elsewhere. As such, Semantic Web technologies shall support the seamless integration of data, on-the-fly composition and interoperation of Web services, as well as more intuitive search engines. The semantics – or meaning – of information, however, cannot be defined without a context, which makes personalization, trust, and provenance core topics for Semantic Web research. New retrieval paradigms, user interfaces, and visualization techniques have to unleash the power of the Semantic Web and at the same time hide its complexity from the user. Based on this vision, the journal welcomes contributions ranging from theoretical and foundational research over methods and tools to descriptions of concrete ontologies and applications in all areas. We especially welcome papers which add a social, spatial, and temporal dimension to Semantic Web research, as well as application-oriented papers making use of formal semantics.
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