面向知识图谱的知识推理综述

Overview on Knowledge Reasoning for Knowledge Graph

随着智能化水平的不断提高,每时每刻都有大量的新知识产生,知识图谱逐渐成为我们管理知识的工具之一.但现有的知识图谱仍然存在属性缺失、关系稀疏等问题,同时还存在大量噪声信息,导致图谱质量不佳,易对自然语言处理领域中的各类任务造成影响.面向知识图谱的知识推理技术作为目前的研究热点,是解决该问题的主要方法,其通过模拟人的推理过程完成对图谱信息的完善,在众多应用中有较好表现.以知识图谱为切入点,将知识推理技术按类别划分并分别阐释,详细分析该技术的几种应用任务,例如智能问答、推荐系统等,最后对未来主要研究方向进行展望,提出几种研究思路.

As the intelligence level grows, a large amount of new knowledge is generated all the time, and knowledge graph has gradually become one of the tools for knowledge management. However, the existing knowledge graph still has some problems, such as missing attributes, sparse relations, and massive noisy information, which leads to poor graph quality and is easy to affect various tasks in the field of natural language processing. As a research hotspot, the knowledge reasoning technology oriented to the knowledge graph is the main method to solve this problem. It improves the information of the knowledge graph by simulating the human reasoning process, with a good performance in many applications. Taking the knowledge graph as the pointcut, this study classifies and explains the knowledge reasoning technology by categories and elaborates on several application tasks of the technology, such as intelligent questionanswering and the recommendation system. Finally, it forecasts the main research directions in the future and puts forward several research ideas.