知识图谱问答(knowledge graph question answering,KGQA)是一种通过处理用户提出的自然语言问题,从知识图谱中获取相关答案的技术.早期的知识图谱问答技术受到知识图谱规模、计算能力以及自然语言处理能力的限制,准确率较低.近年来,随着人工智能技术的进步,特别是大语言模型(large language model,LLM)的发展,知识图谱问答技术的性能得到显著提升.大语言模型如GPT-3等已经被广泛应用于增强知识图谱问答的性能.为了更好地研究学习增强知识图谱问答的技术,对现有的各种大语言模型增强的知识图谱问答方法进行了归纳分析.总结了大语言模型和知识图谱问答的相关知识,即大语言模型的技术原理、训练方法,以及知识图谱、问答和知识图谱问答的基本概念.从语义解析和信息检索两个维度,综述了大语言模型增强知识图谱问答的现有方法,分析了方法所解决的问题及其局限性.收集整理了大语言模型增强知识图谱问答的相关资源和评测方法,并对现有方法的性能表现进行了总结.最后针对现有方法的局限性,分析并提出了未来的重点研究方向.
Overview of Knowledge Graph Question Answering Enhanced by Large Language Models
Knowledge graph question answering(KGQA)is a technology that retrieves relevant answers from a knowledge graph by processing natural language questions posed by users.Early KGQA technologies were limited by the size of knowledge graphs,computational power,and natural language processing capabilities,resulting in lower accuracy.In recent years,with advancements in artificial intelligence,particularly the development of large language models(LLMs),KGQA technology has achieved significant improvements.LLMs such as GPT-3 have been widely applied to enhancing the performance of KGQA.To better study and learn the enhanced KGQA technol-ogies,this paper summarizes various methods using LLMs for KGQA.Firstly,the relevant knowledge of LLMs and KGQA is summarized,including the technical principles and training methods of LLMs,as well as the basic con-cepts of knowledge graphs,question answering,and KGQA.Secondly,existing methods of enhancing KGQA with LLMs are reviewed from two dimensions:semantic parsing and information retrieval.The problems that these methods address and their limitations are analyzed.Additionally,related resources and evaluation methods for KGQA en-hanced by LLMs are collected and organized,and the performance of existing methods is summarized.Finally,the limitations of current methods are analyzed,and future research directions are proposed.
large language modelknowledge graph question answeringsemantic parsinginformation retrieval
万方数据
维普
