系统工程与电子技术 ›› 2022, Vol. 44 ›› Issue (9): 2878-2885.doi: 10.12305/j.issn.1001-506X.2022.09.22

• 系统工程 • 上一篇    下一篇

双向循环进化的实体链接及知识推理框架

封皓君1,*, 段立1, 张碧莹1, 刘海潮1,2   

  1. 1. 海军工程大学电子工程学院, 湖北 武汉 430033
    2. 中国人民解放军91202部队, 辽宁 葫芦岛 125004
  • 收稿日期:2021-06-23 出版日期:2022-09-01 发布日期:2022-09-09
  • 通讯作者: 封皓君
  • 作者简介:封皓君(1997—), 男, 硕士研究生, 主要研究方向为模式识别与智能系统、自然语言处理|段立(1976—), 男, 教授, 博士, 主要研究方向为军事知识工程、信息融合|张碧莹(1997—), 女, 硕士研究生, 主要研究方向为信息对抗、数据挖掘|刘海潮(1994—), 男, 硕士研究生, 主要研究方向为军事知识工程、军事指挥

Bidirectional cyclic evolutionary framework of entity linking and knowledge reasoning

Haojun FENG1,*, Li DUAN1, Biying ZHANG1, Haichao LIU1,2   

  1. 1. College of Electronic Engineering, Naval University of Engineering, Wuhan 430033, China
    2. Unit 91202 of the PLA, Huludao 125004, China
  • Received:2021-06-23 Online:2022-09-01 Published:2022-09-09
  • Contact: Haojun FENG

摘要:

为体系化、智能化提升自然语言处理中实体链接任务的准确率, 同时解决图谱噪声混杂、关系稀疏等问题, 基于第三代人工智能知识&数据双轮驱动思想提出一种双向循环进化的实体链接与知识推理框架。基于知识图谱下的实体链接技术设计正向进化模块, 基于知识推理与元学习等技术设计反向进化模块, 多次循环双向控制过程实现两任务在弱监督下自我迭代和智能升级。实验表明, 在模块化设计加持下, 该框架可从特定领域文本习得特定领域知识, 并实现快速增量更新, 有效提升实体链接、知识推理效率; 同时兼具开放性, 为各业务领域小样本下文本分析能力的迭代升级提供新方法。

关键词: 自然语言处理, 实体链接, 知识推理, 双向控制, 模块化设计

Abstract:

To improve the accuracy of entity linking task in natural language processing in a systematic and intelligent way, and to solve the problems of hybrid graph noise and sparse relationship, a bidirectional cyclic evolutionary framework of entity linking and knowledge reasoning based on the knowledge & data two-wheel driving idea of the third-generation artificial intelligence is proposed. The forward evolution module is designed based on the entity linking technology under knowledge graph, and the reverse evolution module is designed based on the knowledge reasoning and new technologies such as meta-learning. The bidirectional control process is cycled several times to realize self-iteration and intelligent upgrading of two tasks under weak supervision. Experiments show that, with the help of modular design, the framework can acquire domain-specific knowledge from domain-specific texts and rapid incremental update, which can effectively improve the efficiency of entity linking and knowledge reasoning. It also provides a new method for iterative upgrading of text analysis capability under small samples in various fields.

Key words: natural language processing, entity linking, knowledge reasoning, bidirectional control, modular design

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