Learning to match ontologies on the Semantic Web
Learning to match ontologies on the Semantic Web
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DOI：
10.1007/s00778-003-0104-2
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发表时间：
2003-11-01
期刊：
影响因子：
4.2
通讯作者：
Halevy, A
中科院分区：
文献类型：
--
作者：
Doan, A;Madhavan, J;Halevy, A
在语义Web上，数据将不可避免地来自于许多不同的本体，如果不知道它们之间的语义映射，跨本体的信息处理是不可能的。手动查找这样的映射是乏味的、容易出错的，而且显然在Web规模上是不可能的。因此，开发工具，以协助本体映射过程是至关重要的语义Web的成功。我们描述GLUE，一个系统，采用机器学习技术来找到这样的映射。给定两个本体，对于一个本体中的每个概念，GLUE在另一个本体中找到最相似的概念。我们给出了有充分依据的概率定义几个实际的相似性措施，并表明胶水可以与所有这些。GLUE的另一个关键特征是它使用多种学习策略，每种学习策略都很好地利用了数据实例或本体分类结构中不同类型的信息。为了进一步提高匹配精度，我们扩展了GLUE，将常识知识和领域约束纳入匹配过程。因此，我们的方法的区别在于，它与各种定义良好的相似性概念，它有效地结合了多种类型的知识。我们描述了一组实验在几个现实世界的领域，并表明GLUE提出了高度准确的语义映射。最后，我们扩展GLUE找到本体之间的复杂映射，并描述实验表明，该方法的承诺。
On the Semantic Web, data will inevitably come from many different ontologies, and information processing across ontologies is not possible without knowing the semantic mappings between them. Manually finding such mappings is tedious, error-prone, and clearly not possible on the Web scale. Hence the development of tools to assist in the ontology mapping process is crucial to the success of the Semantic Web. We describe GLUE, a system that employs machine learning techniques to find such mappings. Given two ontologies, for each concept in one ontology GLUE finds the most similar concept in the other ontology. We give well-founded probabilistic definitions to several practical similarity measures and show that GLUE can work with all of them. Another key feature of GLUE is that it uses multiple learning strategies, each of which exploits well a different type of information either in the data instances or in the taxonomic structure of the ontologies. To further improve matching accuracy, we extend GLUE to incorporate commonsense knowledge and domain constraints into the matching process. Our approach is thus distinguished in that it works with a variety of well-defined similarity notions and that it efficiently incorporates multiple types of knowledge. We describe a set of experiments on several real-world domains and show that GLUE proposes highly accurate semantic mappings. Finally, we extend GLUE to find complex mappings between ontologies and describe experiments that show the promise of the approach.