Mining the Content of Relational Databases to Learn Ontologies with Deeper Taxonomies
Mining the Content of Relational Databases to Learn Ontologies with Deeper Taxonomies
复制标题
挖掘关系数据库的内容以学习具有更深入分类的本体
DOI：
10.1109/wiiat.2008.382
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
Farid Cerbah
中科院分区：
文献类型：
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作者：
Farid Cerbah
关系数据库是本体学习的宝贵资源。以前的工作表明，如何从这种结构化输入中学习精确的本体。然而，现有方法的一个主要持久限制是派生具有扁平结构的本体，这些本体只是映射源数据库的模式。在本文中，我们提出了RTAXON学习方法，该方法展示了如何利用数据库的内容来识别可以生成类层次结构的分类模式。这种完全形式化的方法结合了经典的模式分析和数据中的层次挖掘。RTAXON是RDBToOnto工具中实现的方法之一。
Relational databases are valuable sources for ontology learning. Previous work showed how precise ontologies can be learned from such structured input. However, a major persisting limitation of the existing approaches is the derivation of ontologies with flat structure that simply mirror the schema of the source databases. In this paper, we present the RTAXON learning method that shows how the content of the databases can be exploited to identify categorization patterns from which class hierarchies can be generated. This fully formalized method combines a classical schema analysis with hierarchy mining in the data. RTAXON is one of the methods implemented in the RDBToOnto tool.