用三词语言融合图模型与大语言模型，提升知识图谱补全效果
GLTW: Joint Improved Graph Transformer and LLM via Three-Word Language for Knowledge Graph Completion
- 设计改进图注意力网络编码局部全局结构信息
- 在多个数据集上超越现有最优方法，显著提升补全精度
- 适合研究知识图谱与大模型融合的开发者参考
知识图谱补全（KGC）旨在推断缺失或不完整的事实，是知识图谱的关键任务。然而，将知识图谱的结构信息有效融入大语言模型（LLM），并实现确定性预测仍具挑战。为此，我们提出新方法GLTW，通过编码知识图谱结构信息并与大语言模型融合，以提升KGC性能。具体地，我们引入改进的图变压器（iGT），可有效编码包含局部与全局结构信息的子图，并继承语言模型特性，无需从头训练。同时，我们设计基于子图的多分类训练目标，将知识图谱中所有实体作为分类对象，提升学习效率。尤为重要的是，我们将iGT与接收知识图谱语言提示的大语言模型结合。在多个知识图谱数据集上的广泛实验表明，相较于当前最优基线，GLTW实现了显著性能提升。
原文摘要 · Abstract (English)
Knowledge Graph Completion (KGC), which aims to infer missing or incomplete facts, is a crucial task for KGs. However, integrating the vital structural information of KGs into Large Language Models (LLMs) and outputting predictions deterministically remains challenging. To address this, we propose a new method called GLTW, which encodes the structural information of KGs and merges it with LLMs to enhance KGC performance. Specifically, we introduce an improved Graph Transformer (iGT) that effectively encodes subgraphs with both local and global structural information and inherits the characteristics of language model, bypassing training from scratch. Also, we develop a subgraph-based multi-classification training objective, using all entities within KG as classification objects, to boost learning efficiency.Importantly, we combine iGT with an LLM that takes KG language prompts as input.Our extensive experiments on various KG datasets show that GLTW achieves significant performance gains compared to SOTA baselines.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。