收稿日期: 2016-05-16
修回日期: 2016-06-25
网络出版日期: 2016-07-20
基金资助
本文系国家自然科学基金项目“基于网络结构演化的Folksonomy模式中社群知识组织与知识涌现研究”（项目编号：71473035）和中国博士后科学基金项目“大数据环境下基于网络演化的领域知识动态剖绘研究”（项目编号：2015M570132）研究成果之一。
Research on the Dynamic Evolution of Domain Knowledge Based on the Network Centrality
Received date: 2016-05-16
Revised date: 2016-06-25
Online published: 2016-07-20
[目的/意义]通过对知识网络中心性的动态分析，揭示领域知识发展过程的规律与模式。此类探究对于洞悉知识发展过程中的衍生、交叉、融合等现象具有重要意义。[方法/过程]以复杂网络分析中的中心性分析技术为主要研究方法，基于领域关键词共现关系构建领域知识网络。从核心涌现性、桥接控制性、关联紧密性3个方面，对特定领域知识发展过程进行时间序列的动态跟踪与分析。[结果/结论]研究结果表明，知识关联关系的增长速度远高于文献与关键词的增长速度；领域中知识的核心涌现程度呈波动状态发展；领域知识之间的桥接控制程度随时间推移呈上升趋势；领域中知识间的关联紧密性在时间轴上逐渐松散。这些领域知识演化规律的揭示，有助于把握领域知识演进的发展脉络，对于揭示知识发展模式与规律具有积极的促进作用。
滕广青, 贺德方, 彭洁, 赵汝南, 张利彪 . 基于网络中心性的领域知识动态演化研究[J]. 图书情报工作, 2016 , 60(14) : 128 -134,141 . DOI: 10.13266/j.issn.0252-3116.2016.14.016
[Purpose/significance] The dynamic analysis of the centrality in knowledge networks can reveal the laws and models of the domain knowledge development. The detection has important significance for the insight into the derived, cross and fusion phenomena in the process of knowledge development.[Method/process] This study used the centrality analysis technology of the complex network analysis as the main research method and structured domain knowledge networks based on co-occurrence relations of domain keywords. The knowledge development process in specific domains was dynamically tracked and analyzed via time series with core-emergence, bridging-control, and relevance-closeness.[Result/conclusion] The results show that the growth rate of knowledge relationships is much higher than the literature and keywords growths; the core-emergence degree of domain knowledge shows a fluctuation growth state; the bridging-control degree between domain knowledge has an upward trend over time; the relevance-closeness degree among domain knowledge gradually lowers along the timeline. These rules of the knowledge evolution help to grasp the venation of the domain knowledge evolution, and play a positive role in revealing knowledge development models and rules.
Key words： domain knowledge; complex network; network centrality; time series
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