| 摘要: |
| 知识图谱可以有效组织和利用现实世界中的各类知识, 弥补数据驱动的方法或者大模型技术存在的不可解释和幻觉等不足, 是实现具有理解和推理能力的认知智能系统的核心技术之一. 知识图谱推理是从已有的知识推理出新的知识, 广泛应用于工农业生产、国防安全、日常生活等领域. 早期知识图谱推理领域主要关注单一推理任务, 近3年逐渐开始关注更加复杂的推理任务, 包括时序多步知识图谱推理、小样本多步知识图谱推理、小样本时序知识图谱推理、小样本多模态知识图谱推理、多模态归纳式知识图谱推理和时序归纳式知识图谱推理. 为此, 首先分别描述面向单一任务和复杂任务的知识图谱推理的特点, 进而, 介绍几类面向单一任务的知识图谱推理技术. 然后系统总结面向复杂任务的已有代表性知识图谱推理方法, 并对这些方法进行对比分析. 最后, 总结和探讨当前知识图谱推理仍存在的主要问题, 并对该领域有价值的未来发展方向进行展望. |
| 关键词: 知识图谱推理 单一任务和复杂任务 知识表示学习 多模态学习 大模型 |
| DOI：10.13328/j.cnki.jos.007627 |
| 分类号:TP18 |
| 基金项目:国家自然科学基金(62376016, U25A20531) |
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| Review of Knowledge Graph Reasoning Research: From Single Tasks to Complex Tasks |
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NIU Guang-Lin, SU Si-Yue, LI Bo
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School of Artificial Intelligence, Beihang University, Beijing 100191, China
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| Abstract: |
| Knowledge graphs effectively organize and exploit diverse real-world knowledge, alleviating limitations of purely data-driven methods and large language models, such as limited interpretability and hallucination, and serve as one of the core technologies for cognitive intelligent systems with understanding and reasoning capabilities. Knowledge graph reasoning aims to infer new knowledge from existing knowledge and is widely applied in various fields, including industrial and agricultural production, national defense and security, and daily life. Early studies on knowledge graph reasoning primarily focus on single tasks, while recent research increasingly shifts attention to more complex tasks. These complex tasks include temporal multi-step knowledge graph reasoning, few-shot multi-step knowledge graph reasoning, few-shot temporal knowledge graph reasoning, few-shot multi-modal knowledge graph reasoning, multi-modal inductive knowledge graph reasoning, and temporal inductive knowledge graph reasoning. Accordingly, this study first describes the characteristics of knowledge graph reasoning for single tasks and complex tasks. Subsequently, several representative knowledge graph reasoning techniques for single tasks are introduced. Then, existing representative knowledge graph reasoning methods for complex tasks and conduct a comparative analysis of these methods are systematically summarized and comparatively analyzed. Finally, the major remaining challenges in knowledge graph reasoning are discussed, and promising future research directions are outlined. |
| Key words: knowledge graph reasoning single task and complex task knowledge representation learning multi-modal learning large language model (LLM) |
