Knowledge Graph Completion (KGC) aims to predict the missing information in the (head entity)-[relation]-(tail entity) triplet. Deep Neural Networks have achieved significant progress in the relation prediction task. However, most existing KGC methods focus on single features (e.g., entity IDs) and sub-graph aggregation, which cannot fully explore all the features in the Knowledge Graph (KG), and neglect the external semantic knowledge injection. To address these problems, we propose MUSE, a knowledge-aware reasoning model to learn a tailored embedding space in three dimensions for missing relation prediction through a multi-knowledge representation learning mechanism. Our MUSE consists of three parallel components: 1) Prior Knowledge Learning for enhancing the triplets' semantic representation by fine-tuning BERT; 2) Context Message Passing for enhancing the context messages of KG; 3) Relational Path Aggregation for enhancing the path representation from the head entity to the tail entity. Our experimental results show that MUSE significantly outperforms other baselines on four public datasets, such as over 5.50% improvement in H@1 and 4.20% improvement in MRR on the NELL995 dataset. The code and all datasets will be released via https://github.com/NxxTGT/MUSE.

本研究针对知识图谱补全(KGC)中现有方法不足以充分利用所有特征及外部语义知识的问题，提出了MUSE模型。MUSE通过多知识表示学习机制，创建了一个三维的专用嵌入空间来预测缺失关系，显示出在四个公开数据集上的显著性能提升，尤其在NELL995数据集上H@1提高了5.50%，MRR提高了4.20%。

MUSE：在边缘上传递多知识，提升知识图谱补全