基于强化学习的知识图谱综述

知识图谱是一种用图结构建模事物及事物间联系的数据表示形式，是实现认知智能的重要基础，得到了学术界和工业界的广泛关注.知识图谱的研究内容主要包括知识表示、知识抽取、知识融合、知识推理4部分.目前，知识图谱的研究还存在一些挑战.例如，知识抽取面临标注数据获取困难而远程监督训练样本存在噪声问题，知识推理的可解释性和可信赖性有待进一步提升，知识表示方法依赖人工定义的规则或先验知识，知识融合方法未能充分建模实体之间的相互依赖关系等问题.由环境驱动的强化学习算法适用于贯序决策问题.通过将知识图谱的研究问题建模成路径(序列)问题，应用强化学习方法，可解决知识图谱中的存在的上述相关问题，具有重要应用价值.首先梳理了知识图谱和强化学习的基础知识.其次，对基于强化学习的知识图谱相关研究进行全面综述.再次，介绍基于强化学习的知识图谱方法如何应用于智能推荐、对话系统、游戏攻略、生物医药、金融、安全等实际领域.最后，对知识图谱与强化学习相结合的未来发展方向进行展望.

Survey of Knowledge Graph Based on Reinforcement Learning

Knowledge graph (KG) is a form of data representation that uses graph structure to model the connections between things. It is an important foundation for realizing cognitive intelligence and has received extensive attention from academia and industry. The research of knowledge graph includes four parts: knowledge representation, knowledge extraction, knowledge fusion, knowledge reasoning. Currently, there are still some challenges in the research of knowledge graphs. For example, knowledge extraction methods face difficulty in obtaining labeled data, while distantly supervised training samples have noise problems. The interpretability and reliability of the knowledge reasoning methods need to be further improved. Knowledge representation methods also have problems such as relying on manually defined rules or prior knowledge. Knowledge fusion methods fail to fully model the interdependence between entities. Environment-driven reinforcement learning (RL) algorithms are suitable for sequential decision-making problems. By modeling the research problem of the knowledge graph into a path (sequence) problem, and applying reinforcement learning methods, the above-mentioned problems in the knowledge graph can be solved, which has important application value. The basic knowledge of KG and RL are introduced firstly. Secondly, a research of KG based on RL are comprehensively reviewed. Then, it focuses on how the KG method based on RL can be applied to practical application areas such as intelligent recommendation, conversation system, game, biology, medicine prediction, finance and cybersecurity. Finally, the future directions of KG and RL are discussed in detail.