Scalable Matching of Industry Models - a Case Study
Scalable Matching of Industry Models - a Case Study
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行业模型的可扩展匹配——案例研究
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发表时间：
2009
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通讯作者：
Min Wang
中科院分区：
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作者：
Brian Byrne;Achille Fokoue;Aditya Kalyanpur;Kavitha Srinivas;Min Wang
最近的一种解决本体匹配问题的方法是将本体匹配问题转化为信息检索问题。我们将探索这种方法在匹配真正的UML、ER、EMF和XML-Schema模型的模型元素方面的效用，在这些模型中，模型的语义定义不太精确。我们与来自不同领域（医疗保健、保险和银行）的行业模型的领域专家一起验证了这种方法。我们还观察到，在该领域，为如此大的行业模型手动构建映射容易出现严重的错误。我们描述了我们开发的一种新工具，用于检测可疑映射，以快速隔离这些错误。
A recent approach to the problem of ontology matching has been to convert the problem of ontology matching to information retrieval. We explore the utility of this approach in matching model elements of real UML, ER, EMF and XML-Schema models, where the semantics of the models are less precisely defined. We validate this approach with domain experts for industry models drawn from very different domains (healthcare, insurance, and banking). We also observe that in the field, manually constructed mappings for such large industry models are prone to serious errors. We describe a novel tool we developed to detect suspicious mappings to quickly isolate these errors.