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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (7): 113-122    DOI: 10.11925/infotech.2096-3467.2018.1355
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Methodology and Tools to Enrich Sci-Tech Big Data
Beibei Kong1,Jing Xie1,2(),Li Qian1,2,Zhijun Chang1,2,Zhenxin Wu1,2
1(National Science Library, Chinese Academy of Sciences, Beijing 100190, China)
2(Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China)
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[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.

Key wordsSci-Tech Big Data      Data Appreciation      Enrichment Method     
Received: 03 December 2018      Published: 06 September 2019
ZTFLH:  TP391  
Corresponding Authors: Jing Xie     E-mail:

Cite this article:

Beibei Kong,Jing Xie,Li Qian,Zhijun Chang,Zhenxin Wu. Methodology and Tools to Enrich Sci-Tech Big Data. Data Analysis and Knowledge Discovery, 2019, 3(7): 113-122.

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