MUSE通过多知识融合提升知识图谱补全效果，显著优于现有方法。
MUSE: Integrating Multi-Knowledge for Knowledge Graph Completion
- 构建三路并行机制：语义先验、上下文传递与关系路径聚合
- 在NELL995数据集上H@1提升超5.50%，MRR提升4.20%
- 适合研究知识图谱补全与多源知识融合的读者
知识图谱补全（KGC）旨在预测头实体--[关系]-->尾实体三元组中缺失的关系。现有方法多聚焦单一特征或子图聚合，未能充分挖掘知识图谱特征，也忽视了外部语义知识的引导作用。为此，我们提出一种感知知识的推理模型MUSE，设计了一种新颖的多知识表征学习机制以实现缺失关系预测。该模型通过三个并行模块构建定制嵌入空间：1）先验知识学习，通过微调BERT增强三元组语义表示；2）上下文消息传递，增强知识图谱中的上下文信息；3）关系路径聚合，增强从头实体到尾实体的路径表示。实验结果表明，MUSE在四个公开数据集上显著优于其他基线模型，在NELL995数据集上实现超过5.50%的H@1提升和4.20%的MRR提升。代码与数据集将通过https://github.com/SUSTech-TP/ADMA2024-MUSE.git发布。
原文摘要 · Abstract (English)
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.
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