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A Graph-based Approach to Person Name Disambiguation in Web

A Graph-based Approach to Person Name Disambiguation in Web 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Management Information Systems (TMIS) Association for Computing Machinery

A Graph-based Approach to Person Name Disambiguation in Web

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2019 ACM
ISSN
2158-656X
eISSN
2158-6578
DOI
10.1145/3314949
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

ACM Transactions on Management Information Systems (TMIS)Association for Computing Machinery

Published: May 17, 2019

Keywords: Person name disambiguation

References