Unsupervised author name disambiguation based on heterogeneous networks

doi:10.3969/j.issn.1000-5641.2021.06.015

Abstract

Abstract:

Author name disambiguation is an important step in constructing an academic knowledge graph. The issue of ambiguous names is widely prevalent in academic literature due to the presence of missing data, ambiguous names, or abbreviations. This paper proposes an unsupervised author name disambiguation method, based on heterogenous networks, with the goal of addressing the problems associated with inadequate information utilization and cold-start; the proposed method automatically learns the features of papers with the ambiguous authors’ name. As a starting point, the method preprocesses strings of authors, organizations, titles, and keywords by lemmatization. The algorithm then learns the embedded representation of text features by the word2vec and TF-IDF methods and learns the embedded representation of structural features using the meta-path random walk and word2vec methods. After merging features by similarity of structure and text, disambiguation is done by a DBSCAN clustering algorithm and merging isolated papers. Experimental results show that the proposed model significantly outperforms existing models in a small dataset and in engineering applications for cold-start unsupervised author name disambiguation. The data indicates that the model is effective and can be implemented in real-world applications.

Key words: author disambiguation, academic knowledge graph, heterogeneous network, meta-path random walk

CLC Number:

TP182

Chenliang GUO, Xin LIN, Yue YIN. Unsupervised author name disambiguation based on heterogeneous networks[J]. Journal of East China Normal University(Natural Science), 2021, 2021(6): 147-160.

Figures/Tables 12

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Experimental results of the AMiner test set"

作者名称	本文方法			AMiner方法^[4]			OAG比赛第一名方法			概率模型方法^[14]			GHOST方法^[11]
作者名称	召回率/%	精确率/%	${F_1}/\%$	召回率/%	精确率/%	${F_1}/\%$	召回率/%	精确率/%	${F_1}/\%$	召回率/%	精确率/%	${F_1}/\%$	召回率/%	精确率/%	${F_1}/\%$
XU Xu	55.85	70.83	62.46	45.86	74.18	56.68	60.39	60.69	60.54	41.87	48.16	44.80	21.79	61.34	32.15
YU Rong	40.31	85.08	54.70	46.51	89.13	61.12	53.55	71.49	61.23	40.85	65.48	50.32	36.41	92.00	52.17
TIAN Yong	62.96	80.01	70.47	51.95	76.32	61.82	63.93	39.35	48.71	56.85	70.74	63.04	54.58	86.94	67.60
HAN Lu	22.39	64.63	33.26	28.05	51.78	36.39	48.31	42.47	45.21	20.62	47.88	28.82	17.39	69.72	27.84
HUANG Lin	44.52	86.29	58.74	32.87	77.10	46.09	59.17	69.84	64.07	34.17	71.84	46.31	17.25	86.15	28.74
XU Kexin	95.12	80.71	87.32	98.64	91.37	94.87	99.65	80.46	89.04	82.47	90.02	86.08	28.52	92.90	43.64
QUAN Wei	39.78	83.81	53.95	39.02	53.88	45.26	56.55	62.09	59.19	47.66	64.45	54.77	27.80	86.42	42.07
DENG Tao	42.13	80.22	54.24	43.62	81.63	56.86	54.18	60.53	57.18	29.89	53.04	38.23	24.50	73.33	36.73
LI Hongbin	70.84	89.10	78.93	69.21	77.20	72.99	72.16	56.64	63.47	53.05	54.66	53.84	29.12	56.29	38.39
BAI Hua	33.81	83.24	48.09	39.73	71.49	51.08	35.84	63.92	45.93	35.90	58.58	44.52	29.54	83.06	43.58
CHEN Meiling	45.07	92.79	60.67	44.70	74.93	55.99	49.57	54.17	51.77	28.80	59.36	38.79	23.85	86.11	37.35
WANG Yanqing	59.32	64.81	91.95	75.33	71.52	73.37	93.58	16.07	27.43	51.97	60.40	55.87	40.39	80.79	53.86
ZHANG Xudong	7.69	61.61	13.67	22.54	62.40	33.12	10.69	43.28	17.14	23.35	70.20	35.04	7.23	85.75	13.34
SHI Qiang	47.07	53.60	50.12	36.15	52.20	42.72	51.77	47.66	49.63	36.94	43.84	40.10	26.80	53.72	35.76
ZHENG Min	11.56	45.13	18.40	22.35	57.65	32.21	16.48	26.05	20.19	19.70	54.76	28.98	15.21	80.50	25.58
15个的平均值	45.23	74.79	56.37	46.44	70.85	56.10	50.05	52.98	51.47	40.27	60.89	48.48	26.69	78.33	39.81
100个的平均值	65.77	75.21	70.17	63.03	77.96	67.79	73.36	60.14	66.10	59.53	70.63	62.81	57.09	77.22	50.23

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ID	关键词	机构	其他作者列表
${P_{\text{1}}}$	adaptive computing allocation, mobile cloud	School of Transportation and Logistics	XING Tianyi; CAI Lin; HUANG Dijiang; PENG Daiyuan; LIU Yan
${P_2}$	adaptive channel allocation, wireless algorithm	National United Engineering Laboratory of Integrated and Intelligent Transportation School	ZHANG Jin; LI Wei; GULLIVER Aaron
${P_3}$	network coding DVB-IPDC LTE	Secure Networking and Computing Institute, Arizona State University	WANG Lian; PENG Daiyuan
${P_{\text{4}}}$	5-axis machining G code, Interpolation NURBS	College of Mechanical Engineering	LI Xia
${P_{\text{5}}}$	5-axis NURBS surfaces STEP-NC	College of Mechanical and Electrical Engineering	LI Xia

数据集	作者名数量	论文数量	消歧后作者数量	机构缺失的论文数量	摘要缺失的论文数量	关键词缺失的论文数量
AMiner	600	203078	39781	134 (0.06%)	3118 (1.53%)	49132 (24%)
AMiner训练集	500	169720	33382	114 (0.06%)	2647 (1.56%)	41299 (24%)
AMiner测试集	100	35023	6399	20 (0.06%)	488 (1.39%)	8286 (24%)
SCI	13328290	18138796	14279136	830942 (5%)	4584943 (25%)	7748879 (43%)
SCI测试集	10	184	44	11 (6%)	62 (33%)	81 (44%)

作者名称	召回率/%	精确率/%	${F_1}/\%$
ABBAS Hazzim	100.00	100.00	100.00
AALKJAER Christian	90.00	100.00	94.74
ABEL Robert	93.69	43.24	59.18
AARABI Mahmoud	100.00	100.00	100.00
AAMIR Muhammad	87.50	100.00	93.33
ABE Yuki	83.33	100.00	90.91
ABBASI Shawn	100.00	100.00	100.00
ABE Kazuo	87.76	100.00	93.48
ABDULLAH Amin	65.22	48.39	55.56
ABAB Julia	80.00	66.67	72.73
平均值	88.75	85.83	87.27

模型类型	AMiner测试集100个作者			AMiner训练集500个作者			AMiner数据平均结果
模型类型	召回率/%	精确率/%	${F_1}/\%$	召回率/%	精确率/%	${F_1}/\%$	召回率/%	精确率/%	${F_1}/\%$
原始模型	65.77	75.21	70.17	64.56	70.21	67.27	64.76	71.04	67.75
只用结构特征	60.10	65.65	62.75	57.43	63.08	60.12	57.88	63.50	60.56
只用文本特征	87.65	41.04	55.90	86.71	38.41	53.24	86.87	38.85	53.69
去除词形还原	61.25	77.97	68.61	59.08	75.83	66.42	59.44	76.19	66.78
去除TF-IDF加权	62.99	75.72	68.77	62.99	69.79	66.22	62.99	70.78	66.66
去除词向量的随机打乱	55.92	78.76	65.40	54.49	76.46	63.63	54.73	76.84	63.92
去除关键词	63.00	75.79	68.81	62.90	69.61	66.08	62.92	70.64	66.56
去除来源	62.24	77.32	68.96	59.35	74.53	66.07	59.83	75.00	66.56
去除摘要	61.18	77.24	68.28	58.75	75.19	65.97	59.16	75.53	66.35

权值 $e$	召回率/%	精确率/%	${F_1}/\%$	权值 $e$	召回率/%	精确率/%	${F_1}/\%$
0.5	61.50	75.73	67.88	2.8	65.42	73.32	69.15
0.9	62.08	76.70	68.62	3.0	65.77	75.21	70.17
1.3	63.48	77.50	69.80	3.5	66.45	71.52	68.90
2.0	62.91	76.62	69.09	4.0	67.33	68.89	68.10
2.5	64.60	73.72	68.86	5.0	69.70	64.73	67.12