本体的集中分类能力:简单存在概念表达的一般框架与研究

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Semantic Web Pub Date : 2023-08-02 DOI:10.3233/sw-233401
V. Svátek, Ondřej Zamazal, Viet Bach Nguyen, J. Ivánek, Ján Kľuka, Miroslav Vacura
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引用次数: 0

摘要

当重用现有本体以RDF发布数据集(或开发新的本体)时,可以优先考虑那些为重要类(表示为焦点类)提供广泛子分类的本体。子类别不仅可以由命名类组成,还可以由复合类表达式组成。我们将给定本体的焦点分类能力的概念定义为,相对于焦点类和概念表达语言,可以从本体的签名中构建的类别的(估计的)加权计数,符合语言,并被焦点类所包含。为了易于处理的初始实验,我们在存在限制的基础上制定了一个受限制的概念表达语言,并启发式地将其映射到本体公理上的语法模式(所谓的FCE模式)。本文利用从本体集合中获得的三个不同的经验来源,研究了所选择的概念表达语言和相关FCE模式的特征:第一,类定义中的概念表达模式频率;二是本体Tbox中FCE模式的出现;最后,对于本体的Tbox(通过FCE模式)生成的类表达式;它们的“意义”由不同的用户组进行评估,从而产生概念表达模式的“质量排序”。然后对互补分析进行比较和总结。为了进一步实验,还实现了一个基于web的原型,该原型涵盖了本体重用的整个过程,从基于关键字的本体搜索到FCP计算,再到本体的选择以及用复合表达式构建的新概念对其进行充实。
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Focused categorization power of ontologies: General framework and study on simple existential concept expressions
When reusing existing ontologies for publishing a dataset in RDF (or developing a new ontology), preference may be given to those providing extensive subcategorization for important classes (denoted as focus classes). The subcategories may consist not only of named classes but also of compound class expressions. We define the notion of focused categorization power of a given ontology, with respect to a focus class and a concept expression language, as the (estimated) weighted count of the categories that can be built from the ontology’s signature, conform to the language, and are subsumed by the focus class. For the sake of tractable initial experiments we then formulate a restricted concept expression language based on existential restrictions, and heuristically map it to syntactic patterns over ontology axioms (so-called FCE patterns). The characteristics of the chosen concept expression language and associated FCE patterns are investigated using three different empirical sources derived from ontology collections: first, the concept expression pattern frequency in class definitions; second, the occurrence of FCE patterns in the Tbox of ontologies; and last, for class expressions generated from the Tbox of ontologies (through the FCE patterns); their ‘meaningfulness’ was assessed by different groups of users, yielding a ‘quality ordering’ of the concept expression patterns. The complementary analyses are then compared and summarized. To allow for further experimentation, a web-based prototype was also implemented, which covers the whole process of ontology reuse from keyword-based ontology search through the FCP computation to the selection of ontologies and their enrichment with new concepts built from compound expressions.
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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.
期刊最新文献
Wikidata subsetting: Approaches, tools, and evaluation An ontology of 3D environment where a simulated manipulation task takes place (ENVON) Sem@ K: Is my knowledge graph embedding model semantic-aware? Using semantic story maps to describe a territory beyond its map NeuSyRE: Neuro-symbolic visual understanding and reasoning framework based on scene graph enrichment
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