Purpose： This study introduces an algorithm to construct tag trees that can be used as a userfriendly navigation tool for knowledge sharing and retrieval by solving two issues of previous studies, i.e. semantic drift and structural skew.Design/methodology/approach： Inspired by the generality based methods, this study builds tag trees from a co-occurrence tag network and uses the h-degree as a node generality metric. The proposed algorithm is characterized by the following four features：（1） the ancestors should be more representative than the descendants,（2） the semantic meaning along the ancestor-descendant paths needs to be coherent,（3） the children of one parent are collectively exhaustive and mutually exclusive in describing their parent, and（4） tags are roughly evenly distributed to their upper-level parents to avoid structural skew. Findings： The proposed algorithm has been compared with a well-established solution Heymann Tag Tree（HTT）. The experimental results using a social tag dataset showed that the proposed algorithm with its default condition outperformed HTT in precision based on Open Directory Project（ODP） classification. It has been verified that h-degree can be applied as a better node generality metric compared with degree centrality.Research limitations： A thorough investigation into the evaluation methodology is needed, including user studies and a set of metrics for evaluating semantic coherence and navigation performance.Practical implications： The algorithm will benefit the use of digital resources by generating a flexible domain knowledge structure that is easy to navigate. It could be used to manage multiple resource collections even without social annotations since tags can be keywords created by authors or experts, as well as automatically extracted from text.Originality/value： Few previous studies paid attention to the issue of whether the tagging systems are easy to navigate for users. The contributions of this study are twofold：（1） an algori
构建了一种新型文献检索系统,能够摘要一篇文献中引起读者研究工作关注的那些内容,并返回读者对这些内容的评论,从而帮助用户快速了解该文献的学术价值及不足之处等重要信息。利用文献间的引用关系从其他文献中找到指向一篇文献的评论上下文,借鉴查询-检索模式,将评论转化为一元语言模型所生成的查询,并将原文献划分为句子所构成的文档集,基于KL-divergence检索模型找到原文献中与评论对应的句子。选取得分最高的若干句子构成体现原文献对外影响的摘要。系统基于北京大学研制的智能搜索引擎平台Platform for Applying,Researching And Developing Intelligent Search Engine(PARADISE),具有快速构建可扩展好的优点。
