Knowledge Graph Completion (KGC) aims to predict the missing [relation] part of (head entity)--[relation]->(tail entity) triplet. Most existing KGC methods focus on single features (e.g., relation types) or sub-graph aggregation. However, they do not fully explore the Knowledge Graph (KG) features and neglect the guidance of external semantic knowledge. To address these shortcomings, we propose a knowledge-aware reasoning model (MUSE), which designs a novel multi-knowledge representation learning mechanism for missing relation prediction. Our model develops a tailored embedding space through 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. The experimental results show that MUSE significantly outperforms other baselines on four public datasets, achieving over 5.50% H@1 improvement and 4.20% MRR improvement on the NELL995 dataset. The code and datasets will be released via https://github.com/SUSTech-TP/ADMA2024-MUSE.git.

本研究解决了现有知识图谱补全（KGC）方法未充分利用知识图谱特征及外部语义知识的问题。提出了一种知识感知推理模型（MUSE），通过三种并行组件设计多知识表示学习机制来预测缺失关系。实验证明，MUSE在多个数据集上显著优于其他基线模型，提升了关系预测的准确性。

MUSE：融合多知识的知识图谱补全