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

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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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Acknowledgments

This paper is supported by National Nature Science Foundation (No. 61175068), and the National Innovation Fund for Technology based Firms (No. 11C26215305905), and the Open Fund of Software Engineering Key Laboratory of Yunnan Province (No. 2011SE14), and the Ministry of Education of Returned Overseas Students to Start Research and Fund Projects.

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Correspondence to Zhengtao Yu.

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Jiang, J., Yan, X., Yu, Z. et al. A Chinese expert disambiguation method based on semi-supervised graph clustering. Int. J. Mach. Learn. & Cyber. 6, 197–204 (2015). https://doi.org/10.1007/s13042-014-0255-z

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  • DOI: https://doi.org/10.1007/s13042-014-0255-z

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