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基于图对比注意力网络的知识图谱补全

刘丹阳 方全 张晓伟 胡骏 钱胜胜 徐常胜

刘丹阳, 方全, 张晓伟, 等 . 基于图对比注意力网络的知识图谱补全[J]. 北京航空航天大学学报, 2022, 48(8): 1428-1435. doi: 10.13700/j.bh.1001-5965.2021.0523
引用本文: 刘丹阳, 方全, 张晓伟, 等 . 基于图对比注意力网络的知识图谱补全[J]. 北京航空航天大学学报, 2022, 48(8): 1428-1435. doi: 10.13700/j.bh.1001-5965.2021.0523
LIU Danyang, FANG Quan, ZHANG Xiaowei, et al. Knowledge graph completion based on graph contrastive attention network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(8): 1428-1435. doi: 10.13700/j.bh.1001-5965.2021.0523(in Chinese)
Citation: LIU Danyang, FANG Quan, ZHANG Xiaowei, et al. Knowledge graph completion based on graph contrastive attention network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(8): 1428-1435. doi: 10.13700/j.bh.1001-5965.2021.0523(in Chinese)

基于图对比注意力网络的知识图谱补全

doi: 10.13700/j.bh.1001-5965.2021.0523
基金项目: 

国家自然科学基金 62072456

国家自然科学基金 62036012

之江实验室开放课题 2021KE0AB05

详细信息
    通讯作者:

    方全, E-mail: qfang@nlpr.ia.ac.cn

  • 中图分类号: TP391.1

Knowledge graph completion based on graph contrastive attention network

Funds: 

National Natural Science Foundation of China 62072456

National Natural Science Foundation of China 62036012

Open Research Projects of Zhejiang Lab 2021KE0AB05

More Information
  • 摘要:

    知识图谱(KG)补全旨在通过知识库中已知三元组来预测缺失的链接。由于大多数方法都是独立地处理三元组,而忽略了知识图谱所具有的异质结构和相邻节点中固有的丰富的信息,导致不能充分挖掘三元组的特征。考虑基于端到端的知识图谱补全任务,提出了一种图对比注意力网络(GCAT),通过注意力机制同时捕获局部邻域内实体和关系的特征,并封装实体邻域上下文信息。为了有效封装三元组特征,引入一个子图级别的对比训练对象用于增强生成的实体嵌入的质量。为了验证GCAT的有效性,在链接预测任务上评估了所提方法,实验结果表明,在数据集FB15k-237中,MRR比InteractE提高0.005,比A2N模型提高0.042;在数据集WN18RR中,MRR比InteractE提高0.019,比A2N模型提高0.032。实验证明提出的GCAT模型能够有效预测知识图谱中缺失的链接。

     

  • 图 1  图对比注意力网络模型架构

    Figure 1.  Model architecture of graph contrastive attention network

    表  1  数据集统计数据

    Table  1.   Statistics of datasets

    数据集 Ne Nr Train Valid Test
    FB15k-237 14 541 237 272 115 17 535 20 466
    WN18RR 40 943 11 86 835 3 034 3 134
    下载: 导出CSV

    表  2  数据集FB15k-237的实验结果

    Table  2.   Experimental results on FB15k-237 dataset

    模型 MRR Hit@1 Hit@3 Hit@10
    TransE[3] 0.294 0.465
    DistMult[4] 0.241 0.155 0.263 0.419
    ComplEx[5] 0.247 0.158 0.276 0.428
    ConvE[6] 0.325 0.237 0.356 0.501
    RotatE[21] 0.338 0.241 0.375 0.533
    R-GCN[10] 0.248 0.417
    HypER[22] 0.341 0.252 0.376 0.520
    TuckER[23] 0.358 0.266 0.394 0.544
    A2N[11] 0.317 0.232 0.348 0.486
    CompGCN[12] 0.355 0.264 0.390 0.535
    InteractE[7] 0.354 0.263 0.535
    GCAT 0.359 0.269 0.395 0.540
    下载: 导出CSV

    表  3  数据集WN18RR的实验结果

    Table  3.   Experimental results on WN18RR dataset

    模型 MRR Hit@1 Hit@3 Hit@10
    TransE[3] 0.226 0.501
    DistMult[4] 0.430 0.390 0.440 0.490
    ComplEx[5] 0.440 0.410 0.460 0.510
    ConvE[6] 0.430 0.400 0.440 0.520
    RotatE[21] 0.476 0.428 0.492 0.571
    R-GCN[10] 0.137
    HypER[22] 0.465 0.436 0.477 0.522
    TuckER[23] 0.470 0.443 0.482 0.526
    A2N[11] 0.450 0.420 0.460 0.510
    CompGCN[12] 0.479 0.443 0.494 0.546
    InteractE[7] 0.463 0.430 0.528
    GCAT 0.482 0.447 0.495 0.546
    下载: 导出CSV

    表  4  GCAT和其变体方法在数据集FB15k-237上的实验结果

    Table  4.   Experimental results of GCAT and its variant on FB15k-237 dataset

    方法 MRR Hit@1 Hit@3 Hit@10
    GCAT-wo 0.357 0.266 0.392 0.540
    GCAT 0.359 0.269 0.395 0.540
    下载: 导出CSV

    表  5  GCAT和其变体方法在数据集WN18RR上的实验结果

    Table  5.   Experimental results of GCAT and its variant on WN18RR dataset

    方法 MRR Hit@1 Hit@3 Hit@10
    GCAT-wo 0.475 0.441 0.489 0.541
    GCAT 0.482 0.447 0.495 0.546
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-09-06
  • 录用日期:  2021-09-17
  • 刊出日期:  2021-11-02

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