鉴于大多数现有的知识图谱补全方法采用独立处理三元组的方式,而忽略邻域关系和实体对中心实体的不同贡献度的问题,提出了REGNN的图神经网络模型。该模型从邻域内的关系和实体中获得的特征信息被嵌入到中心实体的更新中,通过聚合实体和关系特征来丰富中心实体的表征。实验结果显示,与传统的图神经网络模型相比,在FB15K-237数据集上,REGNN模型的MMR和Hits@10指标分别提高了3.3%和1.5%,在WN18RR数据集上分别提高了1.4%和3.6%,从而验证了该模型的有效性。
Given that the majority of existing knowledge graph completion methods adopt an independent processing approach for triplets, overlooking the varying contributions of neighborhood relations and entities to the central entity,this paper introduces a graph neural network model called REGNN that integrates neighborhood relations and entities. In this model, feature information from relations and entities within the neighborhood is incorporated into the central entity′s update, enriching the representation of the central entity through the aggregation of entity and relation features. Experimental results demonstrate that, in comparison to traditional graph neural network models, REGNN model achieves improvements of 3.3% and 1.5% in terms of the MMR and Hits@10 metrics on the FB15K-237 dataset, and improvements of 1.4% and 3.6% on the WN18RR dataset, thus validating the effectiveness of REGNN model.
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