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基于实例的本体匹配的实证研究

An empirical study of instance-based ontology matching
课程网址: http://videolectures.net/iswc07_schlobach_esib/  
主讲教师: Stefan Schlobach
开课单位: 阿姆斯特丹自由大学
开课时间: 2008-02-01
课程语种: 英语
中文简介:
基于实例的本体映射是一类有前途的本体对齐问题的解决方案。至关重要的是,它依赖于测量注释实例集之间的相似性。本文研究了共现测度的选择对基于实例的映射性能的影响。为此,我们实施了许多不同的统计共现措施。我们已经准备了一个广泛的测试用例,使用了数千个术语、数百万个实例和数十万个共注释项的词汇表。我们已经获得了人类对部分地图空间的黄金标准判断。然后,我们研究了与金标准相比,基准数据集上不同的共现度量和一些算法变化是如何执行的。我们的系统研究表明,基于实例的匹配总体上取得了很好的结果,其中更简单的度量往往优于更复杂的统计共现度量。
课程简介: Instance-based ontology mapping is a promising family of solutions to a class of ontology alignment problems. It crucially depends on measuring the similarity between sets of annotated instances. In this paper we study how the choice of co-occurrence measures affects the performance of instance-based mapping. To this end, we have implemented a number of different statistical cooccurrence measures. We have prepared an extensive test case using vocabularies of thousands of terms, millions of instances, and hundreds of thousands of co-annotated items. We have obtained a human Gold Standard judgement for part of the mapping-space. We then study how the different co-occurrence measures and a number of algorithmic variations perform on our benchmark dataset as compared against the Gold Standard. Our systematic study shows excellent results of instance-based match- ing in general, where the more simple measures often outperform more sophisticated statistical co-occurrence measures.
关 键 词: 本体映射; 统计共生措施; 基准数据集
课程来源: 视频讲座网
最后编审: 2019-12-04:lxf
阅读次数: 50