• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2012, Vol. 34 ›› Issue (4): 171-176.

• 论文 • 上一篇    下一篇

中文新闻事件本体建模与自动扩充

王伟1,2,赵东岩2   

  1. (1.武警工程大学电子技术系,陕西 西安 710086;2.北京大学计算机科学研究所,北京 100871)
  • 收稿日期:2011-11-05 修回日期:2012-02-10 出版日期:2012-04-26 发布日期:2012-04-25
  • 基金资助:

    高等学校博士学科点专项科研基金资助课题(20100001120029);武警工程大学基础研究基金资助项目(WGY-201022)

Chinese News Event Ontology Construction and Autopopulation

WANG Wei1,2,ZHAO Dongyan2   

  1. (1.Department of Electronics Technology,Engineering University of the Chinese Armed Police Force,Xi’an 710086;
    2.Institute of Computer Science and Technology,Peking University,Beijing 100871,China)
  • Received:2011-11-05 Revised:2012-02-10 Online:2012-04-26 Published:2012-04-25

摘要:

针对中文新闻事件的语义层次自动理解问题,给出了新闻事件的定义,构造了一种基于本体的新闻事件模型NOEM。NOEM利用事件的类型、时间、空间、结构、因果、媒体六个方面特征描述新闻事件的5W1H(Who, What, Whom, When, Where and How)语义要素。将抽取的关键事件语义要素自动扩充到本体中后,可构成事件知识库支持事件语义层次的应用。与现有事件模型的比较以及实际应用结果显示,NOEM能够有效描述单个新闻文档中的关键事件、语义要素以及它们之间的关联,具有很强的形式化知识表达、应用集成和扩展能力。

关键词: 5W1H, 本体, 事件模型, 本体扩充

Abstract:

Systematically and semantically modeling the 5W1H (What, Who, When, Where, Why and How) elements of a news article is a fundamental step towards automatic understanding of Chinese news events. In this paper, we propose an ontologybased event model, NOEM, which can semantically describe the 5W1H elements of an event and further organize them in the form of an ontology. The proposed model defines the concepts of entities (time, person, location, organization, etc.), events and their relationship in order to capture the different natures of news events, including temporal, spatial, causal aspects and so on. Additionally the wellorganized event elements are essential for building an event knowledge base which can significantly facilitate online news browsing and managing in various applications. A comparison to the existing event models and an empirical case study show that NOEM can effectively model the semantic elements of news events and their relationship; and has a strong ability to represent the knowledge facts and easily adapt to new domains.

Key words: 5W1H;ontology;event model;ontology population