Learning Ontologies for the Semantic Web
Learning Ontologies for the Semantic Web
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语义网学习本体
DOI：
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发表时间：
2001
期刊：
影响因子：
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通讯作者：
Steffen Staab
中科院分区：
文献类型：
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作者：
A. Maedche;Steffen Staab
语义Web在很大程度上依赖于结构化底层数据的形式本体，以实现全面和可移植的机器理解。因此，语义Web的成功在很大程度上取决于本体的扩散，这需要快速和容易的工程本体和避免知识获取瓶颈。
本体学习极大地方便了本体工程师构建本体。我们在这里提出的本体学习的愿景包括一些互补的学科，这些学科以不同类型的非结构化、半结构化和完全结构化数据为基础，以支持半自动、合作的本体工程过程。我们的本体学习框架通过本体导入，提取，修剪，细化和评价，本体工程师提供了丰富的协调工具本体建模。除了一般的框架和体系结构，我们在本文中显示了一些示例性的技术，在本体学习周期，我们已经实现了我们的本体学习环境，文本到Onto，如本体学习从自由文本，从字典，或从遗留本体，并参考一些其他需要补充完整的体系结构，例如从数据库模式或从XML文档中学习本体的逆向工程。
The Semantic Web relies heavily on the formal ontologies that structure underlying data for the purpose of comprehensive and transportable machine understanding. Therefore, the success of the Semantic Web depends strongly on the proliferation of ontologies, which requires fast and easy engineering of ontologies and avoidance of a knowledge acquisition bottleneck.
Ontology Learning greatly facilitates the construction of ontologies by the ontology engineer. The vision of ontology learning that we propose here includes a number of complementary disciplines that feed on different types of unstructured, semi-structured and fully structured data in order to support a semi-automatic, cooperative ontology engineering process. Our ontology learning framework proceeds through ontology import, extraction, pruning, refinement, and evaluation giving the ontology engineer a wealth of coordinated tools for ontology modeling. Besides of the general framework and architecture, we show in this paper some exemplary techniques in the ontology learning cycle that we have implemented in our ontology learning environment, Text-To-Onto, such as ontology learning from free text, from dictionaries, or from legacy ontologies, and refer to some others that need to complement the complete architecture, such as reverse engineering of ontologies from database schemata or learning from XML documents.