摘要
为实现多领域海量网页信息的有效抽取,以中文知识图谱CN-DBpedia为基础设计Web信息抽取系统。基于知识图谱对网页数据项进行自动标注,建立具有容错能力的包装器归纳框架,从包含错误的标注集中归纳学习出正确的包装器。实验结果表明,该系统的准确率和召回率均高于传统人工标注方法,可显著降低网页信息抽取过程中的人力成本,灵活运用于大规模、多领域的网页信息抽取任务。
In order to effectively extract huge amounts of Web information in multiple fields, a Web information extraction system is designed based on Chinese knowledge graph, CN-DBpedia. Firstly,webpage data items with noise are automatically labeled based on knowledge graph. Then, correct wrappers are induced and learned from labeling sets with errors by a fault-tolerant wrapper induction framework. Experimental results demonstrate that,compared with traditional information extraction method by manual annotation, the proposed system has higher precision and recall rate. It can significantly reduce human participation during the extraction process and flexibly apply to large-scale webpage information extraction tasks in multiple fields.
出处
《计算机工程》
CAS
CSCD
北大核心
2017年第6期118-124,共7页
Computer Engineering
基金
上海市科技创新行动计划基础研究项目(15JC1400900)
上海市自然科学基金(13ZR1417700)
关键词
知识图谱
多领域
WEB信息抽取
网页自动标注
容错
包装器归纳框架
knowledge graph
multi-field
Web information extraction
automatic webpage labeling
fault-tolerance
wrapper induction framework
作者简介
王辉（1980-），女，副教授，主研方向为数据挖掘.
硕士研究生.
洪宇，硕士研究生。
肖仰华，副教授、博士、博士生导师。
