论文标题
Comdense：关系意识的密集嵌入和知识图完成的共同特征
ComDensE : Combined Dense Embedding of Relation-aware and Common Features for Knowledge Graph Completion
论文作者
论文摘要
现实世界知识图（kg）主要是不完整的。恢复缺失关系的问题（称为KG完成）最近已成为一个活跃的研究领域。知识图（kg）嵌入是实体和关系的低维表示，是kg完成的关键技术。诸如凸，SACN，Interacte和RGCN等模型中的卷积神经网络取得了最新成功。本文采用了不同的建筑视图，并提出了使用密集的神经网络结合关系感知和共同特征的Comdense。在关系感知的特征提取中，我们试图通过应用针对每个关系的编码函数来创建关系电感偏差。在共同的特征提取中，我们将共同的编码函数应用于所有输入嵌入。这些编码功能是使用密集的密集层实现的。 Comdense在MRR方面实现了链接预测中最新的性能，与以前的基线方法相比，在FB15K-237上击中@1，在WN18RR上达到@1。我们进行了广泛的消融研究，以检查关系感知层和comdense的共同层的影响。实验结果表明，在Comdense中实现的密集建筑的结合实现了最佳性能。
Real-world knowledge graphs (KG) are mostly incomplete. The problem of recovering missing relations, called KG completion, has recently become an active research area. Knowledge graph (KG) embedding, a low-dimensional representation of entities and relations, is the crucial technique for KG completion. Convolutional neural networks in models such as ConvE, SACN, InteractE, and RGCN achieve recent successes. This paper takes a different architectural view and proposes ComDensE which combines relation-aware and common features using dense neural networks. In the relation-aware feature extraction, we attempt to create relational inductive bias by applying an encoding function specific to each relation. In the common feature extraction, we apply the common encoding function to all input embeddings. These encoding functions are implemented using dense layers in ComDensE. ComDensE achieves the state-of-the-art performance in the link prediction in terms of MRR, HIT@1 on FB15k-237 and HIT@1 on WN18RR compared to the previous baseline approaches. We conduct an extensive ablation study to examine the effects of the relation-aware layer and the common layer of the ComDensE. Experimental results illustrate that the combined dense architecture as implemented in ComDensE achieves the best performance.
