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结合协同发生的实体连接和语义度量
Combining a co-occurrence-based and a semantic measure for entity linking |
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| 课程网址: |
http://videolectures.net/eswc2013_fetahu_entity_linking/
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| 主讲教师: |
Besnik Fetahu
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| 开课单位: |
汉诺威莱布尼兹大学
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| 开课时间: |
2013-07-08
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| 课程语种: |
英语
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| 中文简介: |
语义 web 的一个关键特征在于能够链接相关的 web 资源。然而, 尽管特定数据集中的关系通常是明确定义的, 但不同数据集和 web 资源公司之间的链接却很少见。跨域引用数据集 (如 freebase 和 dbpedia) 的使用日益广泛, 用于对数据集和文档进行注释和丰富, 从而为利用它们固有的语义关系来协调不同的 web 提供了机会资源。在本文中, 我们提出了一种综合方法来发现不同实体之间的关系, 这些实体利用 (a) 参考数据集的图形分析以及 (b) 在搜索引擎的帮助下在 web 上共同出现的实体。在 (a) 中, 我们介绍了一种从社交网络理论中采用和应用的新方法来测量参考数据集中给定实体之间的连通性。连接措施用于标识连接的 web 资源。最后, 我们提出了一个彻底的评价我们的方法使用公开的数据集, 并介绍了与该领域的既定措施的比较。
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| 课程简介: |
One key feature of the Semantic Web lies in the ability to link related Web resources. However, while relations within particular data sets are often well-defined, links between disparate data sets and corpora of Web resources are rare. The increasingly widespread use of cross-domain reference data sets, such as Freebase and DBpedia for annotating and enriching data sets as well as documents, opens up opportunities to exploit their inherent semantic relationships to align disparate Web resources. In this paper, we present a combined approach to uncover relationships between disparate entities which exploits (a) graph analysis of reference data sets together with (b) entity co-occurrence on the Web with the help of search engines. In (a), we introduce a novel approach adopted and applied from social network theory to measure the connectivity between given entities in reference data sets. The connectivity measures are used to identify connected Web resources. Finally, we present a thorough evaluation of our approach using a publicly available data set and introduce a comparison with established measures in the field.
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| 关 键 词: |
大数据; 语义Web; 数据集
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-06-04：毛岱琦(课程编辑志愿者)
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| 阅读次数: |
37
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