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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 2/2015

01-04-2015 | Original Article

A Chinese expert disambiguation method based on semi-supervised graph clustering

Authors: Jin Jiang, Xin Yan, Zhengtao Yu, Jianyi Guo, Wei Tian

Published in: International Journal of Machine Learning and Cybernetics | Issue 2/2015

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Abstract

In order to utilize the associated relationship in the expert page efficiently, we’d like to introduce a Chinese expert disambiguation method based on the semi-supervised graph clustering with the integration of various associated relationships. Firstly, extract the correlation characteristics of the expert attributes according to the correlation analysis on the expert page. Secondly, construct a similarity matrix between the documents on different expert pages with the utilization of the attributes characteristics and the associated relationship of the expert pages. Finally, with the adoption of the attribute correlation as the semi-supervised constraint, construct an expert disambiguation model by applying the graph-based clustering approach to get the solution of the model through the kernel-based method for the purpose to achieve expert name disambiguation. Through the contrast experiment in the Chinese expert disambiguation, it turns out that the disambiguation effect is much better with the adoption of the semi-supervised graph clustering method that has been integrated with the expert-associated relationships.
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Metadata
Title
A Chinese expert disambiguation method based on semi-supervised graph clustering
Authors
Jin Jiang
Xin Yan
Zhengtao Yu
Jianyi Guo
Wei Tian
Publication date
01-04-2015
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 2/2015
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-014-0255-z

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