MUSE：集成多知识以实现知识图谱补全
MUSE: Integrating Multi-Knowledge for Knowledge Graph Completion
AI总结：
提出知识感知推理模型MUSE，通过先验知识学习、上下文消息传递和关系路径聚合三个并行组件融合多知识表示，在四个数据集上显著提升了知识图谱补全性能。
摘要：
知识图谱补全（KGC）旨在预测缺失的[关系]部分，即(头实体)--[关系]->(尾实体)三元组。大多数现有的KGC方法侧重于单一特征（例如关系类型）或子图聚合。然而，它们没有充分探索知识图谱（KG）的特征，并忽略了外部语义知识的指导。为了解决这些缺陷，我们提出了一种知识感知推理模型（MUSE），该模型设计了一种新颖的多知识表示学习机制用于缺失关系预测。我们的模型通过三个并行组件开发了定制的嵌入空间：1）先验知识学习，通过微调BERT来增强三元组的语义表示；2）上下文消息传递，用于增强KG的上下文消息；3）关系路径聚合，用于增强从头实体到尾实体的路径表示。实验结果表明，MUSE在四个公共数据集上显著优于其他基线，在NELL995数据集上实现了超过5.50%的H@1提升和4.20%的MRR提升。代码和数据集将通过https://github.com/SUSTech-TP/ADMA2024-MUSE.git发布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
