摘要
事件泛化是构建事理图谱关键步骤,当前基于聚类和分类的事件泛化框架忽视了领域知识的结构特征和动态变化,难以适用于限定域的事件泛化。本研究提出一个基于深度语义匹配的限定域事件泛化框架,该框架由深度语义计算和种子事件匹配两个模块组成,能有效解决领域知识动态融合和事件语义对齐等问题。以旅游领域数据为例,通过实验证明该事件泛化框架较之于聚类和分类框架具有更好的准确性、稳定性和迁移能力。
Event generalization is a key step in constructing event evolutionary graphs.However,the current frameworks for clustering-based and classification-based event generalization ignore the structural characteristics and dynamic changes of domain knowledge,and are thus unsuitable for event generalization in a limited domain.In this paper,we propose a domain-oriented event generalization framework based on deep semantic matching,comprised of two modules:deep semantic computing and seed event matching.This framework can effectively solve the problems of dynamic fusion of domain knowledge and event semantic alignment.Using tourism data as an example,the event generalization framework demonstrates better accuracy,stability,and migration ability than the existing clustering-and classification-based frameworks.
作者
曹高辉
任卫强
丁恒
Cao Gaohui;Ren Weiqiang;Ding Heng(School of Information Management,Central China Normal University,Wuhan 430079;Hubei Data Governance and Intelligent Decision Research Center,Wuhan 430079)
出处
《情报学报》
CSSCI
CSCD
北大核心
2020年第8期863-871,共9页
Journal of the China Society for Scientific and Technical Information
基金
国家社会科学基金项目“基于事理图谱的社会化问答知识组织与服务研究”(19BTQ075)。
关键词
事件泛化
事理图谱
深度学习
深度语义匹配
event generalization
event evolutionary graph
deep learning
deep semantic matching
