摘要
一个准确丰富的人物关系图谱不仅能够为大众提供人物实体的清晰介绍和人物之间的相互关联,而且能够为智能服务系统提供有效的知识支持.目前大多知识来源均以百科类表格数据为起点,在此基础上构建知识图谱.本文主要描述如何充分利用百科类文本数据构建高质量的人物关系图谱.为解决表格数据中存在属性缺失和错误的问题,我们采用模式匹配和深度学习模型相结合的策略从文本数据中自动学习属性值,进行属性补全和纠错,有效提高了知识图谱的覆盖率和正确率.
An accurate and rich inter-personal relation knowledge graph(KG)not only provides a clear introduction of persons and the interconnections among them but also provide knowledge support for the intelligent service system.At present,most KGs are based on the encyclopedia tabular data.In this article,we mainly describe how to make full use of encyclopedic text to build a high-quality inter-personal relation KG.For solving the problem of missing attributes and errors in tabular data,we propose a method of combining pattern matching and deep learning models to extract attribute information from text data for attribute identification.The experimental results show that our method can effectively improve the coverage and accuracy of KGs.
作者
杨一帆
马进
王海涛
何正球
陈文亮
张民
Yifan YANG;Jin MA;Haitao WANG;Zhengqiu HE;Wenliang CHEN;Min ZHANG(School of Computer Science and Technology,Soochow University,Suzhou 215006,China)
出处
《中国科学：信息科学》
CSCD
北大核心
2020年第7期1003-1018,共16页
Scientia Sinica(Informationis)
基金
国家自然科学基金(批准号:61525205,61876115)资助项目。
关键词
知识图谱
人物关系图谱
属性补全与纠错
信息抽取
knowledge graph
inter-personal relation knowledge graph
attribute identification
information extraction
