摘要
从海量的学术文献中自动发现有价值的高质量文献和研究点的时序演变路径是现代学术趋势分析领域的重要研究内容。本文探讨了一种将引文分析技术、语义本体技术和可视化展示技术进行有效结合的学术文献关键路径自动识别方法和可视化呈现方法，通过结合时间维度，它可以更好帮助学者用户发现有价值的高质量文献群及其相关联系。该方法主要建立在基于振荡算法的学术文献权值算法，和利用基于引文关键词加权共现技术的领域本体设计的引文链接权值算法之上，同时提供了完整的可视化展示界面。最后，文章对相关测试实验做了详细的说明。
Automatic recognition of valuable and high-quality documents and timing evolution path of research contents from the mass academic documents is an important research content in academic trend analysis field. This paper proposes an automatic recognition and visualization method of main-path in academic documents which combines many effective technologies such as citation analysis, semantic ontology and visualization. Through the time dimension, this method can help researchers to find valuable and high-quality documents set and inter-relation. Two main algorithms constitute the base of this method, which are academic documents＇ weight algorithm based on vibration algorithm, and citation links＇ weight algorithm using domain ontology based on citation keywords co-occurrence technology. And a complete visualization interface is also introduced. Finally some related experiments are discussed in detail.
出处
《情报学报》
CSSCI
北大核心
2012年第7期676-685,共10页
Journal of the China Society for Scientific and Technical Information
基金
本文受国家自然科学基金项目“基于通用加权XML模型的个性化用户兴趣本体研究”（项目编号71103081）资助.
关键词
本体
语义分析
引文分析
学术趋势
ontology, semantic analysis, citation analysis, academic trend
