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利用元级学习改进本体匹配
Improving Ontology Matching using Meta-level Learning |
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| 课程网址: |
http://videolectures.net/eswc09_stuckenschmidt_iomu/
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| 主讲教师: |
Heiner Stuckenschmidt
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| 开课单位: |
曼海姆大学
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| 开课时间: |
2009-07-28
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| 课程语种: |
英语
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| 中文简介: |
尽管进行了大量的研究工作，但自动本体匹配仍然存在关于匹配结果质量的严重问题。现有的匹配系统权衡精确度和召回率，并具有其特定的优点和缺点。当必须选择给定任务的正确匹配器时，这会导致问题。在本文中，我们提出了一种改进匹配结果的方法，即不选择特定的匹配器，而是在一组匹配器上应用机器学习技术。因此，我们基于不同匹配器的输出和关于要匹配的元素的性质的附加信息来学习对应的正确性的规则，从而利用单个匹配器的弱点。我们表明，我们的方法总是表现得比使用的匹配器的中值显着更好，并且在大多数情况下优于最佳匹配器，具有给定的一对本体的最佳阈值。作为我们实验的副产品，我们发现大多数投票是一种简单但强大的启发式算法，可以将匹配几乎达到我们学习成果质量的匹配器结合起来。
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| 课程简介: |
Despite serious research efforts, automatic ontology matching still suffers from severe problems with respect to the quality of matching results. Existing matching systems trade-off precision and recall and have their specific strengths and weaknesses. This leads to problems when the right matcher for a given task has to be selected. In this paper, we present a method for improving matching results by not choosing a specific matcher but applying machine learning techniques on an ensemble of matchers. Hereby we learn rules for the correctness of a correspondence based on the output of different matchers and additional information about the nature of the elements to be matched, thus leveraging the weaknesses of an individual matcher. We show that our method always performs significantly better than the median of the matchers used and in most cases outperforms the best matcher with an optimal threshold for a given pair of ontologies. As a side product of our experiments, we discovered that the majority vote is a simple but powerful heuristic for combining matchers that almost reaches the quality of our learning results.
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| 关 键 词: |
自动本体匹配; 匹配器; 机器学习技术
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-04-13：lxf
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| 阅读次数: |
18
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