面向知识图谱的信息抽取技术综述

互联网时代, 数据呈爆发式的增长, 怎样从这些数据中抽取出有用的信息, 已是人工智能研究中的一个核心问题. 知识图谱作为解决这一问题的重要方法, 已成为人工智能技术发展的核心推动力. 信息抽取是知识图谱构建过程中的首要环节, 它实现了从海量的数据中抽取出结构化实体以及实体之间的关系. 本文探讨知识图谱中信息抽取的发展趋势, 对实体抽取、关系抽取和事件抽取及其关键技术进行了综述, 分析和讨论了当前存在的问题、挑战以及未来发展的方向.

Review on Information Extraction Techniques for Knowledge Graph

How to extract useful information from surging data has become a critical issue confronting artificial intelligence in the Internet age. As an important method, knowledge graph has become the main driving force to promote the development of artificial intelligence technology. Information extraction realizes the extraction of structured entities and their relationships from massive data, which is the primary step in constructing a knowledge graph. This study discusses the development trend of information extraction in knowledge graphs, as well as entity extraction, relationship extraction, event extraction, and key technologies. Finally, it analyzes and discusses the current problems, challenges, and future development.