论文标题
图形神经网络中有效的关系感知的邻域聚集通过张量分解
Efficient Relation-aware Neighborhood Aggregation in Graph Neural Networks via Tensor Decomposition
论文作者
论文摘要
已经开发了许多图形神经网络（GNN）来应对知识图嵌入（KGE）的挑战。但是，其中许多方法忽略了关系信息的关键作用，并且将其与实体信息不足相结合，从而导致表达能力降低。在本文中，我们提出了一个新颖的知识图编码器，该编码器将张量分解在关系图卷积网络（R-GCN）的聚合函数中。我们的模型通过采用由关系类型定义的低级张量的投影矩阵来增强相邻实体的表示。这种方法有助于多任务学习，从而产生关系表达。此外，我们通过CP分解引入了核心张量的低排名估计技术，该技术有效地压缩和正规化了我们的模型。我们采用了受对比度学习启发的培训策略，该策略减轻了处理大图固有的1-N方法的训练限制。我们在两个常见的基准数据集（FB15K-237和WN18RR）上胜过所有竞争对手，同时使用低维嵌入的实体和关系。
Numerous Graph Neural Networks (GNNs) have been developed to tackle the challenge of Knowledge Graph Embedding (KGE). However, many of these approaches overlook the crucial role of relation information and inadequately integrate it with entity information, resulting in diminished expressive power. In this paper, we propose a novel knowledge graph encoder that incorporates tensor decomposition within the aggregation function of Relational Graph Convolutional Network (R-GCN). Our model enhances the representation of neighboring entities by employing projection matrices of a low-rank tensor defined by relation types. This approach facilitates multi-task learning, thereby generating relation-aware representations. Furthermore, we introduce a low-rank estimation technique for the core tensor through CP decomposition, which effectively compresses and regularizes our model. We adopt a training strategy inspired by contrastive learning, which relieves the training limitation of the 1-N method inherent in handling vast graphs. We outperformed all our competitors on two common benchmark datasets, FB15k-237 and WN18RR, while using low-dimensional embeddings for entities and relations.
