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Storing fuzzy description logic ontology knowledge bases in fuzzy relational databases

机译:在模糊关系数据库中存储模糊描述逻辑本体知识库

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摘要

In the context of the Semantic Web, fuzzy extensions to OWL (the W3C standard ontology language) and Description Logics (DLs, the logical foundation of OWL) have been extensively investigated, and there are many real fuzzy DL ontology knowledge bases. Therefore, how to store fuzzy DL ontology knowledge bases has become an important issue. In this paper, we propose an approach and implement a tool for storing fuzzy DL ontology knowledge bases in fuzzy relational databases. Our chosen formalism is a fuzzy extension of the very expressive DL SHOIN(D), which is the main logical foundation of the standard ontology language OWL, so that our storage approach can store not only fuzzy DL-knowledge bases but also fuzzy ontology knowledge bases. Firstly, we give a formal definition of fuzzy DL-knowledge bases. In the definition, we consider the constructors of both fuzzy SHOIN(D) DL and fuzzy OWL ontology and add some common fuzzy datatypes (e.g., trapezoidal values, interval values, approximate values, and labels) into the knowledge bases. On this basis, we propose an approach which can store fuzzy DL-knowledge bases in fuzzy relational databases, and provide an example to well explain the approach. The correctness and quality of the storage approach are proved and analyzed. Furthermore, following the proposed approach, we implemented a prototype tool, which can automatically store fuzzy DL-knowledge bases. Finally, we make a discussion about the query problem and make a comparison with the existing works.
机译:在语义Web的上下文中,对OWL的模糊扩展(W3C标准本体语言)和描述逻辑(DLS,猫头鹰的逻辑基础)进行了广泛调查,并且有许多真正的模糊DL本体知识库。因此,如何存储模糊DL本体知识库已成为一个重要问题。在本文中,我们提出了一种方法,实现了一种在模糊关系数据库中存储模糊DL本体知识库的工具。我们所选择的形式主义是一种模糊延伸的非常富有表现力的DL Shoin(D),这是标准本体语言猫头鹰的主要逻辑基础,因此我们的存储方法不仅可以存储模糊DL-KnowledgeAdes,而且可以存储模糊本体知识库。首先,我们给出了模糊DL-KnowledgeS基础的正式定义。在定义中,我们考虑模糊挖掘(D)DL和模糊猫头鹰本体的构造函数,并在知识库中添加一些常见的模糊数据类型(例如,梯形值,间隔值,近似值,近似值)。在此基础上,我们提出了一种方法,可以在模糊关系数据库中存储模糊DL知识库,并提供一个例子来解释方法。证明和分析了存储方法的正确性和质量。此外,遵循所提出的方法,我们实现了一种原型工具,可以自动存储模糊DL-KnowledgeS基础。最后,我们讨论了查询问题并与现有工作进行了比较。

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