知识图谱构建技术:分类、调查和未来方向

Knowledge Graph Construction Techniques:Taxonomy,Survey and Future Directions

知识图谱的概念由谷歌于2012年提出,随后逐渐成为人工智能领域的一个研究热点,已在信息搜索、自动问答、决策分析等应用中发挥作用。虽然知识图谱在各领域展现出了巨大的潜力,但不难发现目前缺乏成熟的知识图谱构建平台,需要对知识图谱的构建体系进行研究,以满足不同的行业应用需求。文中以知识图谱构建为主线,首先介绍目前主流的通用知识图谱和领域知识图谱,描述两者在构建过程中的区别;然后,分类讨论图谱构建过程中存在的问题和挑战,并针对这些问题和挑战,分类描述目前图谱构建过程中的知识抽取、知识表示、知识融合、知识推理、知识存储5个层面的解决方法和策略;最后,展望未来可能的研究方向。

With the concept of knowledge graph proposed by Google in 2012,it has gradually become a research hotspot in the field of artificial intelligence and played a role in applications such as information retrieval,question answering,and decision analysis.While the knowledge graph shows its potential in various fields,it is easy to find that there is no mature knowledge graph construction platform currently.Therefore,it is essential to research the knowledge graph construction system to meet the application needs of different industries.This paper focuses on the construction of the knowledge graph.Firstly,it introduces the current mainstream general knowledge graphs and domain knowledge graphs and describes the differences between the two in the construction process.Then,it discusses the problems and challenges in the construction of the knowledge graph according to various types.To address the above-mentioned issues and challenges,it describes the five-level solution methods and strategies of knowledge extraction,knowledge representation,knowledge fusion,knowledge reasoning,and knowledge storage in the current graph construction process.Finally,it discusses the possible directions for future research on the knowledge graph and its application.