知识图谱补全研究综述

doi:10.3778/j.issn.1673-9418.2502028

摘要/Abstract

摘要： 知识图谱已在众多领域得到广泛应用，显著推进了人工智能相关任务的发展。然而，知识图谱在实际应用中仍面临知识不完备的挑战，这一挑战严重限制了知识图谱在下游任务中的应用效果。知识图谱补全任务能够预测知识图谱中缺失的连接，以解决知识不完备的问题。系统梳理了知识图谱及其补全技术的研究背景，明确了其在人工智能与自然语言处理等领域的关键作用。根据信息来源的不同，将现有补全方法划分为基于结构信息、基于文本信息以及融合结构与文本信息等类型，并对各类方法的代表性成果进行了介绍、优缺点比较及适用场景的归纳，揭示了当前技术的发展脉络与演进趋势。关注多语言知识图谱补全的研究进展，探讨了跨语言实体对齐等关键技术，强调了跨语言知识共享与统一建模的重要性。分析了知识图谱补全在知识融合、知识挖掘等方面的挑战，并展望了未来可能的研究趋势。

关键词: 知识图谱, 知识图谱补全, 图神经网络, 大语言模型, 多语言知识图谱补全

Abstract: Knowledge graphs have been widely applied in various fields and have significantly advanced the development of artificial intelligence tasks. However, knowledge graphs still face the challenge of incompleteness, which severely limits their effectiveness in downstream applications. Knowledge graph completion tasks aim to predict missing links in the graph to address this issue of incompleteness. This paper provides a systematic review of the research background of knowledge graphs and their completion techniques, highlighting their pivotal role in artificial intelligence and natural language processing. Based on different information sources, existing completion methods are categorized into three types: structure-based, text-based, and hybrid methods. It introduces representative results for each category, compares their advantages and disadvantages, and summarizes their applicable scenarios, revealing the current technological development and evolutionary trends. This paper also explores advances in multilingual knowledge graph completion, focusing on key techniques such as cross-lingual entity alignment, and emphasizes the importance of cross-lingual knowledge sharing and unified modeling. Finally, it analyzes the challenges of knowledge graph completion in areas such as knowledge fusion and mining, and outlines future research directions.

Key words: knowledge graph, knowledge graph completion, graph neural networks, large language models, multilingual knowledge graph completion

昂格鲁玛, 王斯日古楞, 斯琴图. 知识图谱补全研究综述[J]. 计算机科学与探索, 2025, 19(9): 2302-2318.

Anggeluma, WANG Siriguleng, SI Qintu. Overview of Research on Knowledge Graph Completion[J]. Journal of Frontiers of Computer Science and Technology, 2025, 19(9): 2302-2318.

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