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自适应特征融合的多模态实体对齐研究

郭浩 李欣奕 唐九阳 郭延明 赵翔

郭浩, 李欣奕, 唐九阳, 郭延明, 赵翔. 自适应特征融合的多模态实体对齐研究. 自动化学报, 2022, 48(x): 1−13 doi: 10.16383/j.aas.c210518
引用本文: 郭浩, 李欣奕, 唐九阳, 郭延明, 赵翔. 自适应特征融合的多模态实体对齐研究. 自动化学报, 2022, 48(x): 1−13 doi: 10.16383/j.aas.c210518
Guo Hao, Li Xin-Yi, Tang Jiu-Yang, Guo Yan-Ming, Zhao Xiang. Adaptive feature fusion for multi-modal entity alignment. Acta Automatica Sinica, 2022, 48(x): 1−13 doi: 10.16383/j.aas.c210518
Citation: Guo Hao, Li Xin-Yi, Tang Jiu-Yang, Guo Yan-Ming, Zhao Xiang. Adaptive feature fusion for multi-modal entity alignment. Acta Automatica Sinica, 2022, 48(x): 1−13 doi: 10.16383/j.aas.c210518

自适应特征融合的多模态实体对齐研究

doi: 10.16383/j.aas.c210518
基金项目: 国家自然科学基金(62002373, 61872446, 71971212, U19B2024)资助
详细信息
    作者简介:

    郭浩:中国人民解放军国防科技大学博士研究生. 主要研究方向为知识图谱构建与融合技术. E-mail: guo_hao@nudt.edu.cn

    李欣奕:中国人民解放军国防科技大学博士. 主要研究方向为自然语言处理和信息检索. 本文通信作者. E-mail: lixinyimichael@163.com

    唐九阳:中国人民解放军国防科技大学教授. 主要研究方向为智能分析, 大数据和社会计算. E-mail: 13787319678@163.com

    郭延明:中国人民解放军国防科技大学副教授. 主要研究方向为深度学习, 跨媒体信息处理与智能对抗. E-mail: guoyanming@nudt.edu.cn

    赵翔:中国人民解放军国防科技大学教授. 主要研究方向为图数据管理与挖掘和智能分析. E-mail: xiangzhao@nudt.edu.cn

Adaptive Feature Fusion for Multi-modal Entity Alignment

Funds: Supported by National Natural Science Foundation of China (62002373, 61872446, 71971212, U19B2024)
More Information
    Author Bio:

    GUO Hao Ph. D. candidate at National University of Defense Technology. His research interest covers knowledge graph construction and fusion

    LI Xin-Yi Ph. D. at National University of Defense Technology. His research interest covers natural language processing and information retrieval. Corresponding author of this paper

    TANG Jiu-Yang Professor at National University of Defense Technology. His research interest covers intelligence analytics, big data and social computing, etc

    GUO Yan-Ming Associate Professor at National University of Defense Technology. His research interest covers deep learning, cross-media processing and adversarial attack

    ZHAO Xiang Professor at National University of Defense Technology. His research interest covers graph data management and mining, intelligence analytics, etc

  • 摘要: 多模态数据间交互式任务的涌现对综合利用不同模态的知识提出了高要求, 多模态知识图谱应运而生, 其通过融合不同模态的知识来满足这类任务的需求. 然而, 现有多模态知识图谱存在图谱知识不完整的问题, 严重阻碍对信息的有效利用. 缓解此问题关键是通过实体对齐方法对图谱进行补全. 当前多模态实体对齐方法以固定权重融合多种模态信息, 在融合过程中忽略了不同模态信息贡献的差异性. 为解决上述问题, 本文设计一套自适应特征融合机制, 根据不同模态数据质量动态融合实体结构信息和视觉信息. 此外, 考虑到视觉信息质量不高、知识图谱之间的结构差异也影响实体对齐的效果, 本文分别设计提升视觉信息有效利用率的视觉特征处理模块以及缓和结构差异性的三元组筛选模块. 在多模态实体对齐任务上的实验结果表明, 本文提出的多模态实体对齐方法的性能优于当前最好的方法.
  • 图  1  知识图谱FreeBase和DBpedia的结构差异性表现

    Fig.  1  Structure difference between knowledge graphs FreeBase and DBpedia

    图  2  自适应特征融合的多模态实体对齐框架

    Fig.  2  Multi-modal entity alignment framework based on adaptive feature fusion

    图  3  视觉特征处理模块

    Fig.  3  Visual feature processing module

    图  4  三元组筛选模块

    Fig.  4  Triples filtering module

    图  5  自适应特征融合与固定权重融合的实体对齐Hits@1对比

    Fig.  5  Entity alignment Hits@1's comparison of adaptive feature fusion and fixed feature fusion

    表  1  多模态知识图谱

    Table  1  Statistic of the MMKGs Datasets

    数据集实体关系三元组图片SameAs
    FB15K14 9151 345592 21313 444
    DB15K14 77727999 02812 84112 846
    Yago15K15 40432122 88611 19411 199
    下载: 导出CSV

    表  2  多模态实体对齐结果

    Table  2  Results of multi-modal entity alignment

    数据集方法seed = 0.2seed = 0.5
    Hits@1Hits@10MRRHits@1Hits@10MRR
    FB15K-DB15KIKRL2.9611.450.0595.5324.410.121
    GCN-align6.2618.810.10513.7934.600.210
    PoE11.117.823.533.0
    HMEA12.1634.860.19127.2451.770.354
    AF2MEA17.7534.140.23329.4550.250.365
    FB15K-Yago15KIKRL3.8412.500.0756.1620.450.111
    GCN-align6.4418.720.10614.0934.800.209
    PoE8.713.318.524.7
    HMEA10.0329.380.16827.9155.310.371
    AF2MEA21.6540.220.28235.7256.030.423
    下载: 导出CSV

    表  3  消融实验实体对齐结果

    Table  3  Entity alignment results of ablation study

    方法$\text{seed} = 0.2$seed = 0.5
    Hits@1Hits@10MRRHits@1Hits@10MRR
    FB15K-DB15K
    AF2MEA17.7534.140.23329.4550.250.365
    AF2MEA-Adaptive16.0331.010.21226.2945.350.331
    AF2MEA-Visual16.1930.710.21226.1445.380.323
    AF2MEA-Filter14.1328.770.19122.9143.080.297
    FB15K-Yago15K
    AF2MEA21.6540.220.28235.7256.250.423
    AF2MEA-Adaptive19.3237.380.25531.7753.240.393
    AF2MEA-Visual19.7536.380.25432.0851.530.388
    AF2MEA-Filter15.8432.360.21627.3848.140.345
    下载: 导出CSV

    表  4  实体视觉特征的对齐结果

    Table  4  Entity alignment results of visual feature

    数据集方法seed = 0.2seed = 0.5
    Hits@1Hits@10MRRHits@1Hits@10MRR
    FB15K-DB15KHMEA-v2.079.820.0583.9114.410.086
    Att8.8120.160.1289.5721.130.139
    Att+Filter8.9820.520.1319.9622.580.144
    FB15K-Yago15KHMEA-v2.7711.490.0724.2815.380.095
    Att9.2521.380.13710.5623.550.157
    Att+Filter9.4321.910.13811.0724.510.158
    下载: 导出CSV

    表  5  不同三元组筛选机制下实体结构特征对齐结果

    Table  5  Entity alignment results of structure feature in different filtering mechanism

    数据集方法seed = 0.2seed = 0.5
    Hits@1Hits@10MRRHits@1Hits@10MRR
    FB15K-DB15KBaseline6.2618.810.10513.7934.600.210
    ${\rm{F}}_{\text{PageRank}}$8.0321.370.12518.9039.250.259
    ${\rm{F}}_{\text{random}}$7.5720.760.12016.3236.480.231
    ${\rm{F}}_{\text{our}}$9.7425.280.15022.0944.850.297
    FB15K-Yago15KBaseline6.4418.720.10615.8836.70.229
    ${\rm{F}}_{\text{PageRank}}$9.5423.450.14421.6742.300.290
    ${\rm{F}}_{\text{random}}$8.1720.860.12618.2238.550.254
    ${\rm{F}}_{\text{our}}$11.5928.440.17524.8847.850.327
    下载: 导出CSV

    表  6  自适应特征融合与固定权重融合多模态实体对齐结果

    Table  6  Multi-modal entity alignment results of fixed feature fusion and adaptive feature fusion

    方法Group1Group2Group3
    Hits@1Hits@10Hits@1Hits@10Hits@1Hits@10
    FB15K-DB15K
    Adaptive16.4432.9717.4333.4719.2935.40
    Fixed13.8728.9115.8231.0818.1234.33
    FB15K-Yago15K
    Adaptive16.4432.9717.4333.4719.2935.40
    Fixed16.2133.2319.5537.1122.2745.52
    下载: 导出CSV

    表  7  补充实验多模态实体对齐结果

    Table  7  Multi-modal entity alignment results of additional experiment

    方法Hits@1Hits@10MRRHits@1Hits@10MRR
    seed = 0.2seed = 0.5
    PoE16.4432.9717.4334.753.60.414
    MMEA13.8728.9115.8240.2664.510.486
    AF2MEA28.6548.220.38248.2575.830.569
    下载: 导出CSV
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  • 收稿日期:  2021-06-09
  • 录用日期:  2021-11-26
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