基于特征编码和图嵌入的姓名消歧方法

doi:10.7523/j.ucas.2020.0019

中国科学院大学学报 ›› 2022, Vol. 39 ›› Issue (3): 360-368.DOI: 10.7523/j.ucas.2020.0019

• 电子信息与计算机科学 • 上一篇下一篇

基于特征编码和图嵌入的姓名消歧方法

马莹莹^1,2,3, 吴幼龙¹, 唐华^1,2,3

1. 上海科技大学信息科学与技术学院, 上海 201210;
2. 中国科学院上海微系统与信息技术研究所, 上海 200050;
3. 中国科学院大学, 北京 100049

收稿日期:2020-02-17 修回日期:2020-04-03 发布日期:2021-05-31
通讯作者: 马莹莹
基金资助:
国家自然科学基金(61901267)资助

Name disambiguation based on encoding attributes and graph topology

MA Yingying^1,2,3, WU Youlong¹, TANG Hua^1,2,3

1 School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China;
2 Shanghai Institute of Microsystem & Information Technology, Chinese Academy of Sciences, Shanghai 200050, China;
3 University of Chinese Academy of Sciences, Beijing 100049, China

Received:2020-02-17 Revised:2020-04-03 Published:2021-05-31

摘要/Abstract

摘要： 针对作者姓名歧义问题,提出基于特征编码和图嵌入的作者姓名消歧方法。该方法首先利用word2vec模型对文档的属性特征进行编码从而构建文档的表征向量,然后采用图自动编码器将文档关系编码至文档向量中,聚类相似文档。为进一步提升聚类结果的准确性,使用图嵌入的方法将文档关系网络和作者关系网络的拓扑结构信息引入文档向量,进一步聚集相关文档。该方法同时利用文档的属性特征以及多个关系网络的信息,通过无监督学习的方法寻找文档表征向量,实现良好的姓名消歧效果。在真实作者数据集AMiner上的测试结果表明,该方法显著优于目前几个其他基于图网络的方法。

关键词: 姓名消歧, 图神经网络, 聚类方法, 特征提取, 图嵌入

Abstract: Aiming at solving the problem of author name ambiguity, we propose a novel name disambiguation method based on encoding attributes and graph topology. A word2vec model is used to construct document representation vectors by encoding the attributes of documents. The relationship of documents is then encoded into the document embedding vectors by a graph auto-encoder and similar documents are aggregated. To further improve the accuracy of the clustering results, a graph embedding model is proposed to introduce the document-document network and author-author network topology into the document vectors afterword, thus related papers are moved closer. This method utilizes the information of document attributes and relationship networks at the same time, finds document representation vectors using an unsupervised model and improves the performance of name disambiguation. Experimental results on the real author dataset AMiner show that our method is superior to several state-of-the-art graph-based solutions.

Key words: name disambiguation, graph neural network, clustering method, feature extraction, graph embedding

中图分类号:

TP391.1

马莹莹, 吴幼龙, 唐华. 基于特征编码和图嵌入的姓名消歧方法[J]. 中国科学院大学学报, 2022, 39(3): 360-368.

MA Yingying, WU Youlong, TANG Hua. Name disambiguation based on encoding attributes and graph topology[J]. Journal of University of Chinese Academy of Sciences, 2022, 39(3): 360-368.

参考文献

[1] Wang J, Berzins K, Hicks D, et al. A boosted trees method for name disambiguation[J]. Scientometrics, 2012, 93(2): 391-411. DOI:10.1007/s11192-012-0681-1.
[2] Tran H N, Huynh T, Do T. Author name disambiguation by using deep neural network [C]//Intelligent Information and Database Systems. ACIIDS 2014. Lecture Notes in Computer Science, Spvinger, Cham. 2014,8397: 123-132. DOI:10.1007/978-3-319-05476-6_13.
[3] Han H, Giles L, Zha H, et al. Two supervised learning approaches for name disambiguation in author citations [C]//Proceedings of the 2004 Joint ACM/IEEE Conference on Digital Libraries, 2004. Tucson, AZ, USA. IEEE, 2004: 296-305. DOI:10.1145/996350.996419.
[4] Cen L, Dragut E C, Si L, et al. Author disambiguation by hierarchical agglomerative clustering with adaptive stopping criterion [C]//Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. Dublin Ireland. New York, NY, USA: ACM, 2013: 741-744. DOI:10.1145/2484028.2484157.
[5] Wu H, Li B, Pei Y J, et al. Unsupervised author disambiguation using Dempster-Shafer theory[J]. Scientometrics, 2014, 101(3): 1955-1972. DOI:10.1007/s11192-014-1283-x.
[6] Song Y, Huang J, Councill I G, et al. Efficient topic-based unsupervised name disambiguation [C]//Proceedings of the 7th ACM/IEEE-CS Joint Conference on Digital Libraries. June 18-23, 2007. Vancouver, BC, Canada. New York: ACM Press, 2007: 342-351. DOI:10.1145/1255175.1255243.
[7] Tang J, Fong A C M, Wang B, et al. A unified probabilistic framework for name disambiguation in digital library[J]. IEEE Transactions on Knowledge and Data Engineering, 2012, 24(6): 975-987. DOI:10.1109/TKDE.2011.13.
[8] Tang J, Qu M, Mei Q Z. PTE: predictive text embedding through large-scale heterogeneous text networks [C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2015: 1165-1174. DOI:10.1145/2783258.2783307.
[9] Levin M, Krawczyk S, Bethard S, et al. Citation-based bootstrapping for large-scale author disambiguation[J]. Journal of the American Society for Information Science and Technology, 2012, 63(5): 1030-1047. DOI:10.1002/asi.22621.
[10] Louppe G, Al-Natsheh H T, Susik M, et al. Ethnicity sensitive author disambiguation using semi-supervised learning [C]//International Conference on Knowledge Engineering and the Semantic Web. Springer, Cham, 2016: 272-287. DOI:10.1007/978-3-319-45880-9_21.
[11] Han H Y, Zha H, Giles C L. Name disambiguation in author citations using a K-way spectral clustering method [C]//Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries (JCDL'05). June 7-11, 2005, Denver, CO, USA. IEEE, 2005: 334-343. DOI:10.1145/1065385.1065462.
[12] Zhang Y T, Zhang F J, Yao P R, et al. Name disambiguation in AMiner: clustering, maintenance, and human in the loop [C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018: 1002-1011. DOI:10.1145/3219819.3219859.
[13] Zhang B C, Hasan M A. Name disambiguation in anonymized graphs using network embedding [C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. Singapore, Singapore. New York, NY, USA: ACM, 2017: 1239-1248. DOI:10.1145/3132847.3132873.
[14] Fan X M, Wang J Y, Pu X, et al. On graph-based name disambiguation[J]. Journal of Data and Information Quality, 2011, 2(2): 1-23. DOI:10.1145/1891879.1891883.
[15] Wang X Z, Tang J, Cheng H, et al. ADANA: active name disambiguation [C]//2011 IEEE 11th International Conference on Data Mining. December 11-14, 2011, Vancouver, BC, Canada. IEEE, 2011: 794-803. DOI:10.1109/ICDM.2011.19.
[16] Shin D, Kim T, Choi J, et al. Author name disambiguation using a graph model with node splitting and merging based on bibliographic information[J]. Scientometrics, 2014, 100(1): 15-50. DOI:10.1007/s11192-014-1289-4.
[17] Mikolov T, Sutskever I, Chen K, et al. Distributed representations of words and phrases and their compositionality [C]//Advances in Neural Information Processing Systems(NIPS). 2013: 3111-3119.
[18] Perozzi B, Al-Rfou R, Skiena S. DeepWalk: online learning of social representations [C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2014: 701-710. DOI:10.1145/2623330.2623732.
[19] Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks [EB/OL]. arXiv:1609.02907. (2016-11-03) [2020-02-10].https://arxiv.org/abs/1609.02907.
[20] Ward Jr J H. Hierarchical grouping to optimize an objective function[J]. Journal of the American statistical association, 1963, 58(301): 236-244. DOI:10.1080/01621459.1963.10500845.
[21] Tang J, Zhang J, Yao L M, et al. ArnetMiner: extraction and mining of academic social networks [C]//Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. August 24-27, 2008. Las Vegas, Nevada, USA. New York, NY, USA: ACM Press, 2008: 990-998. DOI:10.1145/1401890.1402008.
[22] Menestrina D, Whang S E, Garcia-Molina H. Evaluating entity resolution results[J]. Proceedings of the VLDB Endowment, 2010, 3(1/2): 208-219. DOI:10.14778/1920841.1920871.

基于特征编码和图嵌入的姓名消歧方法

Name disambiguation based on encoding attributes and graph topology

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 2

编辑推荐

Metrics

本文评价

访问统计

联系我们

[1]	冯齐心, 杨辽, 王伟胜, 陈桃, 黄双燕. 基于时序光谱重构的卷积神经网络遥感农作物分类[J]. 中国科学院大学学报, 2020, 37(5): 619-628.
[2]	张琦, 陈玉荣, 李建国, 胡云, 许胤龙. 基于多核系统的视频特征提取程序并行化及性能优化方法[J]. 中国科学院大学学报, 2011, 28(4): 531-547.