基于LEBERT的多模态领域知识图谱构建  

Construction of Multi-modal Domain Knowledge Graph Based on LEBERT

作　　者：李华昱[1] 付亚凤 闫阳[1] 李家瑞 LI Hua-Yu;FU Ya-Feng;YAN Yang;LI Jia-Rui(College of Computer Science and Technology,China University of Petroleum,Qingdao 266580,China)

机构地区：[1]中国石油大学(华东)计算机科学与技术学院,青岛266580

出　　处：《计算机系统应用》2022年第11期79-90,共12页Computer Systems & Applications

基　　金：山东省自然科学基金面上项目(ZR2020MF140);中国石油大学(华东)研究生创新基金(22CX04035A)

摘　　要：多模态知识图谱(multi-modal knowledge graph,MMKG)是近几年新兴的人工智能领域研究热点.本文提供了一种多模态领域知识图谱的构建方法,以解决计算机学科领域知识体系庞大分散的问题.首先,通过爬取计算机学科的相关多模态数据,构建了一个系统化的多模态知识图谱.但构建多模态知识图谱需要耗费大量的人力物力,本文训练了基于LEBERT模型和关系抽取规则的实体-关系联合抽取模型,最终实现了一个能够自动抽取关系三元组的多模态计算机学科领域知识图谱.Multi-modal knowledge graph(MMKG)is a new research hotspot in artificial intelligence in recent years.This study provides a construction method for multi-modal domain knowledge graphs to solve the problem that the domain knowledge system of computer science is large and decentralized.Specifically,a systematic MMKG is constructed by crawling the relevant multi-modal data of computer science.However,the construction of an MMKG needs a lot of manpower and material resources.In response,this study trains a model of joint extraction of entities and relations based on the LEBERT model and relation extraction rules and ultimately implements an MMKG of the computer science domain that can automatically extract relation triples.

关 键 词：多模态 知识图谱 领域 LEBERT 关系抽取规则 Lexicon Adapter 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]