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MORE: Toward Improving Author Name Disambiguation in Academic Knowledge Graphs

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Abstract

Author name disambiguation (AND) is a fundamental task in knowledge alignment for building a knowledge graph network or an online academic search system. Existing AND algorithms tend to cause over-splitting and over-merging problems of papers, severely jeopardizing the performance of downstream tasks. In this paper, we demonstrate the problem of paper over-splitting and over-merging when constructing an academic knowledge graph. To address the problems, we systematically investigate and propose a unified architecture, MORE, which utilizes LightGBM and HAC FOR paper clusteRing as well as HGAT for both cluster alignmEnt and knowledge graph representation learning. Specifically, we first propose a novel representation learning method which leverages OAG-BERT to learn paper entity embedding and utilizes SimCSE to regularizes pre-trained embedding anisotropic space. We then apply LightGBM to calculate the similarity matrix of papers through entity embedding. We also use hierarchical agglomerative clustering (HAC) for grouping clusters to alleviate over-merging. Finally, considering co-author relationships, we improve the HGAT model using hard-cross graph attention mechanism to generate semantic and structural embedding. Experimental results on two large real-world datasets show that our proposed method achieves 6%\(\sim\)16% improvement against the baseline models on F1-score.

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Notes

  1. https://www.aminer.cn.

  2. https://www.aminer.cn/whoiswho.

  3. https://www.microsoft.com/en-us/research/project/open-academic-graph/.

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Acknowledgements

This work was supported by National Key R &D Program of China through grant 2021YFB1714800, S &T Program of Hebei through grant 21340301D, Innovation Capability Improvement Plan Project of Hebei Province 22567626H and Hebei Natural Science Foundation of China Grant F2022203072.

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Authors and Affiliations

  1. School of Information Science and Engineering, Yanshan University, Qinhuangdao, China

    Jibing Gong, Xiaohan Fang, Jiquan Peng, Yi Zhao, Jinye Zhao & Chenlong Wang

  2. The Key Lab for Computer Virtual Technology and System Integration, The Key Laboratory for Software Engineering of Hebei Province, Beijing, China

    Jibing Gong, Xiaohan Fang, Jiquan Peng, Yi Zhao, Jinye Zhao & Chenlong Wang

  3. National Engineering Research Center for Risk Perception and Prevention, CAEIT, Beijing, China

    Yangyang Li

  4. School of Cyber Science and Technology, Beihang University, Beijing, China

    Jingyi Zhang

  5. Department of Electrical and Software Engineering, University of Calgary, Calgary, AB, Canada

    Steve Drew

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  1. Jibing Gong
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Gong, J., Fang, X., Peng, J. et al. MORE: Toward Improving Author Name Disambiguation in Academic Knowledge Graphs. Int. J. Mach. Learn. & Cyber. 15, 37–50 (2024). https://doi.org/10.1007/s13042-022-01686-5

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