Exploration of SWRL Rule Bases through Visualization, Paraphrasing, and Categorization of Rules
Exploration of SWRL Rule Bases through Visualization, Paraphrasing, and Categorization of Rules
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DOI：
10.1007/978-3-642-04985-9_23
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发表时间：
2009-11
期刊：
影响因子：
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通讯作者：
Saeed Hassanpour;M. O'Connor;Amar K. Das
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文献类型：
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作者：
Saeed Hassanpour;M. O'Connor;Amar K. Das
规则库越来越多地被用作语义网上的知识内容的储存库。随着这些规则库的大小和复杂性的增加，开发人员和最终用户需要规则抽象的方法来促进规则管理。本文描述了一种基于词法分析和启发式规则集的语义Web规则语言(SWRL)规则提取方法。我们的方法产生一个树数据结构，我们在创建可视化、解释和分类SWRL规则的技术时使用该结构。我们通过将我们的方法应用于几个包含SWRL规则的生物医学本体来评估我们的方法，并展示了结果如何揭示规则库中的规则模式。我们已经实现了我们的方法作为一个插件工具，Protégé-OWL，最广泛使用的语义Web本体建模软件。我们的工具可以让用户快速浏览SWRL规则库中的内容和模式，从而实现他们的获取和管理。
Rule bases are increasingly being used as repositories of knowledge content on the Semantic Web. As the size and complexity of these rule bases increases, developers and end users need methods of rule abstraction to facilitate rule management. In this paper, we describe a rule abstraction method for Semantic Web Rule Language (SWRL) rules that is based on lexical analysis and a set of heuristics. Our method results in a tree data structure that we exploit in creating techniques to visualize, paraphrase, and categorize SWRL rules. We evaluate our approach by applying it to several biomedical ontologies that contain SWRL rules, and show how the results reveal rule patterns within the rule base. We have implemented our method as a plug-in tool for Protégé-OWL, the most widely used ontology modeling software for the Semantic Web. Our tool can allow users to rapidly explore content and patterns in SWRL rule bases, enabling their acquisition and management.