Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (6): 1428-1433.doi: 10.13229/j.cnki.jdxbgxb20210443

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Automatic completion algorithm for missing links in nowledge graph considering data sparsity

Wen-jun WANG1(),Yin-feng YU2,3,4   

  1. 1.College of Computer and Network Engineering，Shanxi Datong University，Datong 037009，China
    2.Department of Computer Science and Technology，Tsinghua University，Beijing 100084，China
    3.State Key Laboratory of Intelligent Technology and Systems，Tsinghua University，Beijing 100084，China
    4.School of Information Science and Engineering，Xinjiang University，Urumqi 830046，China
  • Received:2021-05-19 Online:2022-06-01 Published:2022-06-02

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Abstract:

Aiming at the Yungang Grottoes corpus with sparse data， the completion of sparse data is realized through the artificial understanding of semantic rules， which affects the accuracy of the follow-up work to a certain extent. Therefore， an automatic completion algorithm for missing connection of knowledge atlas considering data sparsity is proposed， and some corresponding contents are also proposed in the text. By setting the neighborhood structure of the data， the knowledge graph embedded representation model based on data sparsity is constructed to extract the unknown relationship of the data. Then， the long-short-term memory network model is used to automatically complete the missing data in the variable-length sequence， and the automatic completion of the missing connection of the knowledge graph is realized. Finally， the algorithm is applied to the construction of the knowledge graph of Yungang Grottoes, it can be seen from the experimental results that on the same database set， considering the data sparsity， the accuracy of the algorithm is up to 95.4%，much higher than other traditional algorithms.

Key words: computer application, data sparsity, knowledge graph, completion algorithm, entity relationship

CLC Number: 

  • TP391.1

Fig.1

Construction flow of entity vector structure"

Fig.2

Overall structure of attention network model"

Fig.3

Structure diagram of relation extractionmodel in knowledge graph"

Table 1

Related parameter values of experimental data set"

参数数值参数数值
实体关系数量24 987路径平均长度4.5
查询关系数量32数据缺失/个320 000
实体数389 115实验数据/个640 000
实体属性数2 137

Table 2

Comparison of test results of differentcompletion algorithms"

算 法准确率/%回收率/%F1-score
本文95.410.90.0277
基于LFM的传统方法79.77.90.0253
基于SVD的传统方法80.26.90.0231
基于UserCF的传统方法81.19.70.0195
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