图 书 馆
基于共现和语义度量的实体链接方法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/ |
| 主讲教师: | Besnik Fetahu |
| 开课单位: | 汉诺威莱布尼茨大学 |
| 开课时间: | 2013-07-08 |
| 课程语种: | 英语 |
| 中文简介: |  语义Web的一项关键功能在于链接相关Web资源的能力。但是，尽管通常很好地定义了特定数据集内的关系，但很少有数据集和Web资源语料库之间的链接。诸如Freebase和DBpedia之类的跨域参考数据集越来越广泛地用于注释和丰富数据集以及文档，这为利用它们固有的语义关系来对齐不同的Web资源提供了机会。在本文中，我们提出了一种发现不同实体之间关系的组合方法，该方法利用（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上同时出现实体。在（a）中，我们介绍了一种从社交网络理论采用和应用的新颖方法，用于测量参考数据集中给定实体之间的连通性。连接性度量用于识别连接的Web资源。最后，我们使用公开的数据集对我们的方法进行了全面的评估，并与该领域已建立的措施进行了比较。 |
| 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. |
| 关 键 词: | 语义关系; 数据集; 语义Web; 搜索引擎; 图形分析 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-05-12：zyk |
| 最后编审: | 2021-05-26：zyk |
| 阅读次数: | 62 |