Extracting Keyphrases to Represent Relations in Social Networks from Web
Extracting Keyphrases to Represent Relations in Social Networks from Web
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发表时间：
2007-01
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通讯作者：
Junichiro Mori;M. Ishizuka;Y. Matsuo
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作者：
Junichiro Mori;M. Ishizuka;Y. Matsuo
社交网络最近已经引起了相当大的兴趣。为了利用社交网络的语义网，一些研究已经检查了自动提取的社交网络。然而，大多数方法已经解决了关系强度的提取。我们的目标是提取嵌入在社交网络中的实体之间的潜在关系。为此，我们提出了一种方法，自动提取标签，描述实体之间的关系。从根本上说，该方法聚类相似的实体对根据其集体上下文在Web文档。从聚类结果中得到关系的描述性标签。所提出的方法是完全无监督的，很容易与现有的社会网络提取方法。我们的实验研究人员社交网络和政治社交网络中的实体实现了高精度和召回率的聚类。实验结果表明，该方法能够提取合适的关系标签来表示社交网络中实体之间的关系。
Social networks have recently garnered considerable interest. With the intention of utilizing social networks for the Semantic Web, several studies have examined automatic extraction of social networks. However, most methods have addressed extraction of the strength of relations. Our goal is extracting the underlying relations between entities that are embedded in social networks. To this end, we propose a method that automatically extracts labels that describe relations among entities. Fundamentally, the method clusters similar entity pairs according to their collective contexts in Web documents. The descriptive labels for relations are obtained from results of clustering. The proposed method is entirely unsupervised and is easily incorporated with existing social network extraction methods. Our experiments conducted on entities in researcher social networks and political social networks achieved clustering with high precision and recall. The results showed that our method is able to extract appropriate relation labels to represent relations among entities in the social networks.