摘要
【目的】更加科学规范地对学者影响力进行评估,从而发现领域专家。【方法】从作者、文献、领域、主题4个维度构建知识超网络模型;结合超网络的度量方法、文献计量法,运用LDA主题模型,借鉴PageRank排序的思路,提出基于知识超网络的领域专家识别方法。【结果】以图书情报领域为例,通过实验进行领域专家识别,并将结果与h指数、p指数、社会网络分析法进行对比,验证了本文方法的有效性及合理性。【局限】只选取部分期刊的论文数据进行实验,排序结果与真实的排序可能有差别;通过LDA主题模型挖掘的领域标签的粒度需要进一步细化。【结论】基于科技文献的知识超网络,探索学术影响力评价的科学范式,为领域专家识别提供了新的思路和方法。
[Objective] This paper evaluates the influence of scholars in a more scientific and standardized way, aiming to find domain experts effectively. [Methods] Firstly, we constructed a knowledge super-network model from four dimensions: author, literature, domain and subject. Secondly, we used the measurement methods for super-network and literature, the LDA model and the PageR ank ranking algorithm, to present a domain expert identification method based on knowledge super-network. [Results] We used library and information science as the field to examine the proposed model and found it yielded better results than h-index, p-index and social network analysis. [Limitations] We only retrieved papers from some journals, which may affect the results with other data. The granularity of mining domain labels through the LDA topic model needs to be refined. [Conclusions] Based on the knowledge super-network of scientific and technological literature, the proposed method could assess the academic impacts effectively, and provides new ideas to identify domain experts.
作者
许鹏程
毕强
Xu Pengcheng;Bi Qiang(School of Management,Jilin University,Changchun 130022,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2019年第11期89-98,共10页
Data Analysis and Knowledge Discovery
关键词
知识超网络
领域专家
超边排序
专家识别
Knowledge Super-Network
Domain Experts
SuperEdgeRank
Expert Identification
