%A Beibei Kong,Jing Xie,Li Qian,Zhijun Chang,Zhenxin Wu %T Methodology and Tools to Enrich Sci-Tech Big Data %0 Journal Article %D 2019 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2018.1355 %P 113-122 %V 3 %N 7 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4678.shtml} %8 2019-07-25 %X

[Objective] This paper tries to address the issues facing sci-tech big data, such as source dispersal, low quality, and poor content. [Methods] We used value-added computing methods, such as data cleansing, entity alignment, entity field fusion, conflict detection, etc., to develop tools for the enrichment of sci-tech big data. [Results] The developed tools achieved entity data alignment at the levels of personnel, organization, conference, journal and relationship among them. The contents of the entity fields were increased by 5 to 10 times, and the entity analysis dimension was increased by 2 to 3 times. [Limitations] The timeliness and standardization of value-added data need to be optimized and improved based on service needs. [Conclusions] The proposed methods and tools enhance the knowledge discovery of the sci-tech big data and intelligent information analysis systems.