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A Graph-based Approach to Person Name Disambiguation in Web

机译：Web中基于图的人名消歧方法

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摘要

This article presents a name disambiguation approach to resolve ambiguities between person names and group web pages according to the individuals they refer to. The proposed approach exploits two important sources of entity-centric semantic information extracted from web pages, including personal attributes and social relationships. It takes as input the web pages that are results for a person name search. The web pages are analyzed to extract personal attributes and social relationships. The personal attributes and social relationships are mapped into an undirected weighted graph, called attribute-relationship graph. A graph-based clustering algorithm is proposed to group the nodes representing the web pages, each of which refers to a person entity. The outcome is a set of clusters such that the web pages within each cluster refer to the same person. We show the effectiveness of our approach by evaluating it on large-scale datasets WePS-1, WePS-2, and WePS-3. Experimental results are encouraging and show that the proposed method clearly outperforms several baseline methods and also its counterparts.
机译：本文提供了一种名称歧义消除方法，用于根据个人所指称的个人名称来解决人员名称与组网页之间的歧义。所提出的方法利用了从网页提取的以实体为中心的语义信息的两个重要来源，包括个人属性和社会关系。它以输入网页作为个人姓名搜索的结果作为输入。分析网页以提取个人属性和社会关系。个人属性和社会关系被映射到一个无向加权图，称为属性关系图。提出了一种基于图的聚类算法，对代表网页的节点进行分组，每个节点均指一个人实体。结果是一组群集，这样每个群集中的网页都指向同一个人。通过对大规模数据集WePS-1，WePS-2和WePS-3进行评估，我们展示了该方法的有效性。实验结果令人鼓舞，并且表明所提出的方法明显胜过几种基准方法及其对应方法。

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