Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (1): 14-20.doi: 10.6040/j.issn.1672-3961.0.2019.415

• Machine Learning & Data Mining • Previous Articles     Next Articles

Entity alignment method based on adaptive attribute selection

Jialin SU1,2(),Yuanzhuo WANG1,Xiaolong JIN1,Xueqi CHENG1   

  1. 1. CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China
    2. School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 101408, China
  • Received:2019-07-22 Online:2020-02-20 Published:2020-02-14
  • Supported by:
    国家重点研发计划项目课题(2016YFB1000902);国家自然科学基金资助项目(61572469);国家自然科学基金资助项目(61772501);国家自然科学基金资助项目(61572473);国家自然科学基金资助项目(91646120)

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Abstract:

Most existing entity alignment methods typically relied on external information and required expensive manual feature construction to complete alignment. Knowledge graph-based methods used only semantic information and failed to use structural information. Therefore, this paper proposed a new entity alignment method based on adaptive attribute selection, training an entity alignment model based on the joint embedding of the two knowledge graphs, which combined the semantic and structural information. Also, this paper proposed the use of strong attribute constraint based on adaptive attribute selection, which could adaptively generate the most effective attribute category and weight, to improve the performance of entity alignment. Experiments on two realistic datasets showed that, compared with traditional methods, the precision of the proposed method was improved by 11%.

Key words: knowledge graph, entity alignment, adaptive attribute selection, joint embedding, strong attribute constraint

CLC Number: 

  • TP391

Fig.1

Entity alignment method based on adaptive attribute selection"

Table 1

Data set for entity alignment"

关系路径 Source1实体数 Source2实体数 训练集三元组数 验证集三元组数 测试集三元组数 实体总数 关系类型总数 属性类型总数 单网络三元组总数
Cora 145 143 76 20 20 1 441 7 10 2 180
Baidu Douban M/TV 762 762 462 150 150 8 143 5 6 27 960

Table 2

Results for entity alignment method based on adaptive attribute selection"

使用方法 Cora Baidu Douban M/TV
准确率/% 召回率/% F1/% 准确率/% 召回率/% F1/%
TransE 87.06 63.79 73.63 98.97 88.58 93.49
cross-KG 92.04 65.69 76.64 99.31 82.76 90.28
SEEA 90.72 75.86 82.64 99.61 90.23 94.69
文献[4] 96.97 72.41 82.91 99.80 94.43 97.04
文献[5] 85.03 85.03 85.03 88.21 88.21 88.21
本研究 98.51 84.62 91.04 98.00 96.00 96.99
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