基于知识库的网络搜索中人的消歧

Q. Vu, Tomonari Masada, A. Takasu, J. Adachi
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引用次数: 6

摘要

由于同名问题,按个人姓名查询的结果通常包含与几个人相关的文档。为了区分涉及不同人的文档,需要一种有效的方法来度量文档的相似度,并找到涉及同一人的文档。以前的一些研究人员已经使用向量空间模型或试图提取共同命名实体来测量相似性。本文提出了一种以Web目录为知识库来查找文档对中的共享上下文,并利用共享上下文度量来确定文档对之间的相似度的方法。实验结果表明,该方法优于向量空间模型方法和命名实体识别方法。
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Disambiguation of People in Web Search Using a Knowledge Base
Results of queries by personal names often contain documents related to several people because of the namesake problem. In order to differentiate documents related to different people, an effective method is needed to measure document similarities and to find documents related to the same person. Some previous researchers have used the vector space model or have tried to extract common named entities for measuring similarities. We propose a new method that uses Web directories as a knowledge base to find shared contexts in document pairs and uses the measurement of shared contexts to determine similarities between document pairs. Experimental results show that our proposed method outperforms the vector space model method and the named entity recognition method.
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