Learning Highly Structured Semantic Repositories from Relational Databases:
Learning Highly Structured Semantic Repositories from Relational Databases:
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从关系数据库学习高度结构化的语义存储库：
DOI：
10.1007/978-3-540-68234-9_57
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发表时间：
2008
影响因子：
3.9
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。已经提出了从这种结构化输入生成本体的方法和工具。然而，一个主要的持续限制是具有平面结构的本体的派生，该本体仅反映源数据库的模式。在本文中，我们展示了如何使用 RDBToOnto 工具通过利用数据库模式和数据，更具体地说，通过识别隐藏在数据中的分类法来导出准确的本体。这个可扩展的工具支持迭代方法，允许通过用户定义的约束逐步细化学习过程。
Relational databases are valuable sources for ontology learning. Methods and tools have been proposed to generate ontologies from such structured input. However, a major persisting limitation is the derivation of ontologies with flat structure that simply mirror the schema of the source databases. In this paper, we show how the RDBToOnto tool can be used to derive accurate ontologies by taking advantage of both the database schema and the data, and more specifically through identification of taxonomies hidden in the data. This extensible tool supports an iterative approach that allows progressive refinement of the learning process through user-defined constraints.