%A Jianhua Liu,Zhixiong Zhang,Qin Zhang %T Revealing Sci-Tech Policy Evolution with Entity Relationship %0 Journal Article %D 2019 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2018.1379 %P 57-67 %V 3 %N 5 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4657.shtml} %8 2019-05-25 %X

[Objective] The paper tries to describe the evolutionary path of science and technology (S&T) policies using knowledge from documents generated in policy promotion. [Methods] We proposed a multi-index model with direct semantic relationship, direct co-occurrence relationship, in-direct co-occurrence relationship and link path attenuation index. The S&T policy entities and their relationships used in the proposed model were extracted from the policy texts. We described the S&T policy evolution paths along with time properties and then analyzed the structural features of policy entities and their relationship. [Results] We found the evolution paths of these policies at different stages, and 80% of the retrieved paths were existing in the real world. [Limitations] The proposed model relies on human comparison and interpretation. Besides, the sample size needs to be expanded. [Conclusions] This study reveals the evolutionary path of S&T policies based on related records. It expands the scope and depth of S&T policy analysis research.