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使用UMLS进行语义匹配

Semantic Matching using the UMLS
课程网址: http://videolectures.net/eswc09_shamdasanis_smutu/  
主讲教师: Jetendr Shamdasani
开课单位: 西英格兰大学
开课时间: 2009-07-28
课程语种: 英语
中文简介:
传统的本体对齐技术使得能够在具有一定置信度值的两个本体中的概念之间建立等价关系。然而,通过语义匹配,不仅可以识别概念之间的等价关系,而且可以识别不太一般和更一般的关系。这是有益的,因为可以在本体之间发现更多表达关系,从而帮助我们以更精细的粒度级别解决不同语义表示之间的异质性。这项工作涉及语义匹配在医学领域的应用。我们已经使用UMLS metathesaurus作为背景资源扩展了SMatch算法在医学领域的功能,因此消除了之前对WordNet的依赖,而WordNet并未以令人满意的方式覆盖医学领域。我们描述了将SMatch算法扩展到医学领域以与UMLS一起使用所需的步骤。我们测试了我们在FMA和MeSH本体子集上的方法的准确性,精确度和召回率显示了我们算法的不同版本在每个数据集上的准确性和覆盖范围。
课程简介: Traditional ontology alignment techniques enable equivalence relationships to be established between concepts in two ontologies with some confidence value. With semantic matching, however, it is possible to identify not only equivalence relationships between concepts, but less general and more general relationships. This is beneficial since more expressive relationships can be discovered between ontologies thus helping us to resolve heterogeneity between differing semantic representations at a finer level of granularity. This work concerns the application of semantic matching to the medical domain. We have extended the SMatch algorithm to function in the medical domain with the use of the UMLS metathesaurus as the background resource, hence removing its previous reliance on WordNet, which does not cover the medical domain in a satisfactory manner. We describe the steps required to extend the SMatch algorithm to the medical domain for use with UMLS. We test the accuracy of our approach on subsets of the FMA and MeSH ontologies, with both precision and recall showing the accuracy and coverage of different versions of our algorithm on each dataset.
关 键 词: 本体对齐技术; 语义匹配; 置信度值
课程来源: 视频讲座网
最后编审: 2019-04-13:lxf
阅读次数: 110