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摘要:
随着电力大数据时代的到来，电网企业在多年的技术监督工作中积累了大量的多模态数据。多模态数据的结构化存储和融合是电力系统有效组织和管理的关键。为融合构建大规模的电力缺陷多模态知识图谱，提出基于多通道图神经网络的多模态实体对齐方法，以有效融合多源电力异构数据。收集电力领域缺陷日志记录，构建电力缺陷多模态知识图谱实体对齐数据集(EKG)，将文本、图像等多模态信息整合到知识图谱中，为实体对齐任务提供丰富的语义信息。多模态数据增加了实体和关系表示的难度，通过挖掘电力领域多模态知识图谱的特征信息，设计属性聚合式对齐方法，利用知识图谱中的多模态属性和结构信息从图像、文本、名称和结构4个维度学习节点表示，解决电力缺陷多模态知识图谱无法有效集成的问题。实验结果表明：所提方法在EKG上取得了最好的效果。
Abstract:With the advent of the era of big data in power, power grid enterprises have accumulated a large number of multi-modal data in years of technical supervision work. The structured storage and fusion of multi-modal data are the keys to the effective organization and management of power systems. In order to fuse and construct a large-scale multi-modal knowledge graph of power defects, a multi-modal entity alignment method based on a multi-channel graph neural network was proposed to effectively fuse heterogeneous data of multi-source power. A multi-modal knowledge graph entity alignment dataset (EKG) for power defects was constructed by collecting logs related to many defects in the power field. Multi-modal information such as text and images was integrated into the knowledge graph, which provided rich semantic information for entity alignment tasks. The multi-modal data increased the representation difficulty of the entities and relationships. By mining the characteristics of the multi-modal knowledge graph in the power field, an attribute aggregation alignment method was designed. The node representation was learned from the four dimensions of image, text, name, and structure by using the multi-modal attributes and structure information in the knowledge graph, solving the problem that the power defects of a multi-modal knowledge graph cannot be integrated effectively. Experimental results show that the proposed method achieves the best performance on EKG.
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Key words:
- power knowledge graph /
- graph neural network /
- entity alignment /
- equipment defect /
- knowledge graph
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图 3 不同对齐种子率下的各模型性能
Figure 3. Performance of each model with different alignment seed rates
图 4 不同模型层数和维度下的本文模型性能
Figure 4. Performance of the proposed model with different layers and dimensions
表 1 电力缺陷多模态知识图谱节点和关系类型
Table 1. Node and relationship types of power defects of multi-modal knowledge graph
节点类型 节点关系 设备 子部件关系 缺陷 缺陷关系 缺陷图片 图片 危害 缺陷危害 成因 引起 措施 具体措施 设备型号 型号 
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表 2 数据集统计信息
Table 2. Dataset statistics
类型 实体类型数 关系类型数 属性类型数 图像数 知识图谱1 3 229 7 24 3 821 知识图谱2 4 821 7 18 5 438
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表 3 电力缺陷多模态知识图谱统计
Table 3. Statistics of power defects of multi-modal knowledge graph
类型 关系数 节点数 属性数 知识图谱1 3 498 2 363 3 077 知识图谱2 3 531 2 363 3 132
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表 4 本文方法主要参数
Table 4. Main parameters of the proposed method
参数 本文方法最优值 每批次训练样本的大小 32 节点嵌入维数 128 随机失活比率 0.6 GCN和GAT模型层数 2 训练轮次 100 L2正则化 0.0001 Adam优化器中的初始学习率 0.001
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表 5 实体对齐性能
Table 5. Entity alignment performances
% 模型 MR MRR Hits@1 Hits@10 GCN-align[22] 51.37 60.73 50.92 77.38 KDCoE[23] 33.83 53.35 36.83 60.24 MuGNN[24] 42.82 47.23 47.85 68.76 SEA[25] 28.23 64.27 51.21 70.72 MultiKE[26] 41.78 54.93 51.19 75.25 RSN4EA[27] 32.72 49.02 52.72 73.82 AliNet[28] 38.83 61.38 53.48 75.63 AttrGNN[29] 28.28 62.82 53.05 75.42 本文模型 26.24 66.32 57.82 81.73
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表 6 消融实验
Table 6. Ablation experiments
% 数据格式 信息 MR MRR Hits@1 Hits@10 使用1种数据格式 图像信息（G1） 40.23 42.73 31.32 62.89 文本信息（G2） 43.83 39.51 34.27 65.73 名字信息（G3） 38.29 40.36 38.92 67.28 结构信息（G4） 39.27 41.64 42.03 69.20 使用2种数据格式 G1+G2 33.02 50.72 49.24 73.54 G1+G3 36.71 52.20 51.53 77.09 G1+G4 32.46 53.01 51.01 74.37 G2+G3 30.06 56.24 50.23 75.40 G2+G4 34.12 58.91 52.17 77.74 G3+G4 31.58 57.54 53.92 78.37 使用3种数据格式 G1+G2+G3 29.70 62.87 53.27 79.73 G1+G2+G4 28.06 65.29 56.84 80.28 G1+G3+G4 28.25 62.86 54.24 81.07 G3+G3+G4 26.94 59.72 55.39 80.94 使用所有数据格式 G1+G2+G3+G4 26.24 66.32 57.82 81.73
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