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Data Analysis and Knowledge Discovery  2024, Vol. 8 Issue (3): 10-28    DOI: 10.11925/infotech.2096-3467.2023.0753
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An Overview of Research on Knowledge Graph Completion Based on Graph Neural Network
Wu Yue1,Sun Haichun1,2()
1School of Information and Cyber Security, People’s Public Security University of China, Beijing 100038, China
2Key Laboratory of Security Technology & Risk Assessment, Beijing 100026, China
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Abstract  

[Objective] This paper summarizes the knowledge graph completion methods based on graph neural network through research and literature review. [Coverage] With “knowledge graph completion” as search terms to retrieve literature from the Web of Science, DBLP and CNKI, a total of 79 representative literature were screened out for review. [Methods] Based on the model structure, three knowledge graph completion methods based on graph neural networks were summarized, including graph convolutional neural networks, graph attention networks, and graph auto encoder. [Results] Using common data sets and evaluation indicators for knowledge graph completion tasks, the effects of various models were comparatively analyzed in terms of MRR, MR, Hit@k and other performance evaluations, and prospects for future research were suggested. [Limitations] In the comparison of experimental results, only the evaluation results of some widely used models on the FB15K-237 and WN18RR datasets are discussed, the comparison of all models on the same dataset is lacking. [Conclusions] Compared with the representation learning model and the neural network model, the graph neural network model has better performance, but it still faces difficulties such as high complexity of model relationships, over-smoothness, and poor scalability and universality.
Key words： Knowledge Graph Completion      Graph Neural Network      Graph Convolutional Neural Network      Graph Attention Network      Graph Auto Encoder Network     
Received: 05 August 2023      Published: 12 April 2024
ZTFLH:  TP391  
Fund:Special Funds for Basic Scientific Research Business Expenses of Central Universities(2022JKF02015);Technical Research Program of the Ministry of Public Security of China(2020JSYJC22)
Corresponding Authors: Sun Haichun，E-mail： sunhaichun@ppsuc.edu.cn。   
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Cite this article:

Wu Yue, Sun Haichun. An Overview of Research on Knowledge Graph Completion Based on Graph Neural Network. Data Analysis and Knowledge Discovery, 2024, 8(3): 10-28.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2023.0753     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2024/V8/I3/10

A Knowledge Graph Fragment
分类 子类 经典模型
基于表示学习 平移距离结构 TransE[29]、TransH[33]、TransR[34]、RotatE[35]
张量分解 RESCAL[36]、DisMult[37]、ComplEx[38]、TuckER[39]
基于神经网络 基于卷积神经网络 ConvE[40]、InteractE[41]、ConvKB[42]
基于循环神经网络 RSN[43]
基于强化学习 DeepPath[44]
基于BERT KG-BERT[45]
基于图神经网络 基于图卷积神经网络 R-GCN[28]、RA-GCN[46]、SACN[47]、PGE-WGCN[48]、TransGCN[49]、VR-GCN[50]、COMPGCN[51]、KE-GCN[52]、GGSHCN[53]
基于图注意力网络 KBGAT[54]、ICGAT[55]、GS-InGAT[24]、RGHAT[56]、LSA-GAT[57]、GAFM[58]、DisenKGAT[59]、DRR-GAT[60]、GCAT[61]、MRGAT（1）[22]、MRGAT（2）[23]、HRGAT[62]、MA-GNN[25]
基于图自动编码网络 VGAE[63]、GAEAT[64]、R-GAE[65]、M2GNN[66]、GATE[67]
Classification of Knowledge Graph Completion Methods
Development of the Main Models for Knowledge Graph Completion
Encoder-Decoder Framework
Evolution of GCN Model
Updating a Single Graph Node in R-GCN and RA-GCN Models
Schematic Diagram of GAT Model Evolution
Evolution of GAE Model
评估指标 详细计算公式 公式说明 运用任务
MRR M R R = 1 | ? S ? | ∑ i = 1 | ? S ? | 1 r a n k i ? = ? 1 | ? S ? | 1 r a n k 1 + 1 r a n k 2 + ? + 1 r a n k | ? S ? | 查询集S正确元素的排名记为k，其对应的积分为k的倒数 链接预测、关系预测
MR M R = 1 | ? S ? | ∑ i = 1 | ? S ? | r a n k i = 1 | ? S ? | ( r a n k 1 + r a n k 2 + ? + r a n k | ? S ? | ) 查询集S正确元素的排名记为k 链接预测、关系预测
Hits@k H i t s @ k = 1 | ? S ? | ∑ i = 1 | ? S ? | I ( r a n k | ? s ? | ≤ k ) Ⅰ[x]为指示函数，若条件真则为1，否则为0 链接预测、关系预测
MAP M A P = 1 S ∑ i = 1 S A P S APS 为查询集S的平均精度 链接预测、关系预测
Accuracy A c c = T P + T N T P + T N + F P + F N TP： True Positive（真阳性），FP： False Positive（假阳性），FN： False Negative（假阴性），TN： True Negative（真阴性） 实体分类
Recall R e c a l l = T P T P + F N 三元组中有多少正样本被正确预测 实体分类
Precision P r e c i s i o n = T P T P + F P 预测为正的三元组中有多少是真正的正样本 实体分类
Detailed Computing Formulas of Evaluation Metrics
数据集 实体
个数		 关系类型个数 训练集三元组个数 验证集三元组个数 测试集三元组个数
FB15K-237 14 541 237 272 115 17 535 20 466
FB15K 14 951 1 345 483 142 50 000 59 071
WN18 40 943 18 141 442 5 000 5 000
WN18RR 40 943 11 86 835 3 034 3 134
Datasets for Knowledge Map Completion Tasks
模型 MRR MR Hit@10 Hit@3 Hit@1 模型 MRR MR Hit@10 Hit@3 Hit@1
TransE[29] 0.294 357 0.465 GGSHCN-ConvE[53] 0.337 0.506 0.370 0.254
DisMult[37] 0.241 254 0.419 0.263 0.155 GGSHCN-DisMult[53] 0.331 0.534 0.399 0.271
ComplEx[38] 0.247 339 0.428 0.275 0.158 DisenKGAT[59] 0.368 179 0.553 0.407 0.275
ConvE[40] 0.325 244 0.501 0.356 0.237 MRGAT（2）[23] 0.358 0.542 0.386 0.266
ConvKB[42] 0.243 311 0.421 0.371 0.155 GCAT[61] 0.359 0.540 0.395 0.269
R-GCN[28] 0.248 0.417 HRGAT[62] 0.366 156 0.542 0.404 0.271
SACN[47] 0.350 0.540 0.390 0.260 M2GNN[66] 0.362 0.565 0.398 0.275
VR-GCN[50] 0.248 0.432 0.272 0.159 DRR-GAT[60] 0.361 298 0.549 0.415 0.268
COMPGCN[51] 0.355 197 0.535 0.390 0.264 GS-InGAT[24] 0.382 109 0.567 0.416 0.283
RA-GCN[46] 0.249 0.417 MA-GNN[25] 0.379 145 0.569 0.415 0.282
Performance of Knowledge Graph Completion Model on the FB15K-237 Dataset
模型 MRR MR Hit@10 Hit@3 Hit@1 模型 MRR MR Hit@10 Hit@3 Hit@1
TransE[29] 0.226 3 384 0.501 GGSHCN-ConvE[53] 0.497 0.583 0.534 0.442
DisMult[37] 0.430 5 110 0.490 0.440 0.390 GGSHCN-DisMult[53] 0.502 0.571 0.513 0.448
ComplEx[38] 0.440 5 261 0.510 0.460 0.410 DisenKGAT[59] 0.486 1 504 0.578 0.502 0.441
ConvE[40] 0.430 4 187 0.520 0.440 0.400 MRGAT（2）[23] 0.481 0.568 0.501 0.443
ConvKB[42] 0.249 3 324 0.524 0.471 0.057 GCAT[61] 0.482 0.546 0.495 0.447
R-GCN[28] 0.137 HRGAT[62] 0.491 2 685 0.567 0.503 0.454
SACN[47] 0.470 0.540 0.480 0.430 M2GNN[66] 0.485 0.572 0.498 0.444
VR-GCN[50] DRR-GAT[60] 0.468 2 596 0.578 0.507 0.421
COMPGCN[51] 0.479 3 533 0.546 0.494 0.443 GS-InGAT[24] 0.546 1 323 0.625 0.556 0.491
RA-GCN[46] MA-GNN[25] 0.565 886 0.679 0.592 0.507
Performance of Knowledge Graph Completion Model on the WN18RR Dataset
模型 MUTAG/% AM/%
R-GCN[28] 73.23 89.29
SACN[47] 77.90 90.20
COMPGCN[51] 85.30 90.60
KE-GCN[52] 91.20
Entity Classification Results（Accuracy）
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