基于信息增强和子图对齐的知识图推理
IEEE transactions on neural networks and learning systems
|November 7, 2025
概括
这项研究引入了一种新的知识图推理 (KGR) 方法,LSA,它用大语言模型 (LLM) 增强图形. 通过对齐文本和结构信息,LSA提高了图形的完整性和准确性.
科学领域:
- 人工智能的人工智能
- 数据挖掘 数据挖掘
- 自然语言处理自然语言处理.
背景情况:
- 知识图推理 (KGR) 对于数据挖掘至关重要,旨在推断出新的事实,以确保图的完整性和准确性.
- 大型语言模型 (LLM) 越来越多地被整合到基线模型中,但它们在KGR中的应用需要进一步探索.
- 现有的LLM增强的KGR模型提出了需要创新解决方案的挑战.
研究的目的:
- 提出一种新的知识图推理 (KGR) 方法,LSA,利用大型语言模型 (LLM) 来增强信息.
- 通过整合LLM生成的文本描述来提高知识图的准确性和完整性.
- 通过信息增强和子图对齐的综合战略,解决当前LLM增强的KGR方法的局限性.
主要方法:
- LSA使用LLM来生成图实体,关系和子图的文本描述.
- 显式利用:LLM生成的文本特征被用作现有的KGR模型的初始特征.
- 隐式利用:一个学习机制调整关键子图的结构和文本信息.
主要成果:
- 在三个标准数据集上对LSA进行了评估,显示出有前途的性能.
- 该方法有效地将知识图 (KG) 与来自LLMs的信息进行丰富.
- 与LSA集成的表示学习模型显示了表达能力的提高.
结论:
- LSA成功地利用LLM来丰富知识图表,从而实现更具信息性的表示.
- 子图对齐机制增强了结构和文本信息的整合.
- 拟议的方法为推进LLM增强的知识图推理提供了一种可行的方法.
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