Please wait a minute...
Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (1): 15-26    DOI: 10.11925/infotech.2096-3467.2018.1354
Current Issue | Archive | Adv Search |
Building Knowledge Graph with Sci-Tech Big Data
Ying Wang1(),Li Qian1,2,Jing Xie1,2,Zhijun Chang1,2,Beibei Kong1
1National Science Library, Chinese Academy of Sciences, Beijing 100190, China
2Department of Library, Information and Archives Management, University of Chinese Academy of Sciences, Beijing 100190, China
Download: PDF (3962 KB)   HTML ( 36
Export: BibTeX | EndNote (RIS)      

[Objective] This paper tries to extract information from Sci-Tech big data and build an academic knowledge network, aiming to develop smart knowledge services. [Methods] We proposed an Ontology schema and a framework to contruct knowledge graph based on the distributed storage and high-performance computing of big data platform. The proposed model helped us extract and align research entities for relationship discovery. We also adopted the knowledge merging and enrichment, semantic storage and quality management techniques. [Results] We created a huge knowledge graph including more than 300 million entities and 1.1 billion relations. It also supported knowledge discovery platform and smart personal research assistant apps for scientific big data. [Limitations] More research is needed to improve the quality management of knowledge graph, as well as the precision of entity alignment. [Conclusions] The proposed method improve the knowledge management of scientific and technology big data.

Key wordsSci-Tech Big Data      Knowledge Graph      Ontology      Knowledge Extraction     
Received: 03 December 2018      Published: 04 March 2019

Cite this article:

Ying Wang,Li Qian,Jing Xie,Zhijun Chang,Beibei Kong. Building Knowledge Graph with Sci-Tech Big Data. Data Analysis and Knowledge Discovery, 2019, 3(1): 15-26.

URL:     OR

[1] Singhal A. Introducing the Knowledge Graph: Things, Not Strings[EB/OL]. [2013-04-10]. .
[2] Wu W, Li H, Wang H, et al.Probase: A Probabilistic Taxonomy for Text Understanding[C]// Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data. New York: ACM, 2012: 481-492.
[3] Baidu Open Knowledge Graph[EB/OL]. [2018-08-16]..
[4] 张阔. 从搜索信息到搜索知识——技术架构[EB/OL]. [2013-03-26]. .
[4] (Zhang Kuo. From Information Search to Knowledge Search — Technology Infrastructure[EB/OL]. [2013-03-26].
[5] 王元卓, 贾岩涛, 赵泽亚, 等. OpenKN-网络大数据时代的知识计算引擎[J]. 中国计算机学会通讯, 2014, 10(11): 30-35.
[5] (Wang Yuanzhuo, Jia Yantao, Zhao Zeya, et al.OpenKG-Knowledge Computing Engine in the Era of Network Big Data[J]. Communications of the Chinese Computer Federation, 2014, 10(11): 30-35.)
[6] Zhu J G, Wang H F, Shen B J. Software. Zhishi.Schema: A Software Programming Taxonomy Derived from Stackoverflow[C]// Proceedings of the 14th International Semantic Web Conference (ISWC 2015), Bethlehem, Pennsylvania, USA. 2015: 1-4.
[7] Introduction to CN-Probase [EB/OL]. [2017-11-29]. .
[8] 国务院. 新一代人工智能发展规划[R]. [2017-07-08]. 新一代人工智能发展规划[R]. [2017-07-08]. .
[8] (State Council.New Generation Artificial Intelligence Development Plan[R]. [2017-07-08]. New Generation Artificial Intelligence Development Plan[R]. [2017-07-08].
[9] 钱力, 谢靖, 常志军, 等. 基于科技大数据的智能知识服务体系研究设计[J]. 数据分析与知识发现. DOI: 10.11925/infotech.2096-3467.2018.1364.
[9] (Qian Li, Xie Jing, Chang Zhijun, et al.Designing Smart Knowledge Services with Sci-Tech Big Data[J]. Data Analysis and Knowledge Discovery. DOI: 10.11925/infotech.2096-3467. 2018.1364.)
[10] Springer Nature.SN SciGraph [EB/OL]. [2018-08-18]..
[11] Allen B P.The Roll of Metadata in the Second Machine Age [EB/OL]. [2017-02-02]..
[12] Taylor & [EB/OL].[2018-05-05]. .
[13] Tang J, Zhang J, Yao L M, et al.AMiner: Extraction and Mining of Academic Social Networks[C]// Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD’2008). Las Vegas, Nevada, USA. New York, ACM, 2008:990-998.
[14] Acemap Knowledge Graph[EB/OL]. [2018-05-05]..
[15] 国家科技文献中心. NSTL统一文献元数据标准3.0[EB/OL]. [2017-10-18]. .
[15] (National Science and Technology Library. Unified MetaData Standard for Scientific Literature Version3.0 [EB/OL]. [2017-10-18].
[1] Zhou Yang,Li Xuejun,Wang Donglei,Chen Fang,Peng Lijuan. Visualizing Knowledge Graph for Explosive Formula Design[J]. 数据分析与知识发现, 2021, 5(9): 42-53.
[2] Shen Kejie, Huang Huanting, Hua Bolin. Constructing Knowledge Graph with Public Resumes[J]. 数据分析与知识发现, 2021, 5(7): 81-90.
[3] Ruan Xiaoyun,Liao Jianbin,Li Xiang,Yang Yang,Li Daifeng. Interpretable Recommendation of Reinforcement Learning Based on Talent Knowledge Graph Reasoning[J]. 数据分析与知识发现, 2021, 5(6): 36-50.
[4] Li He,Liu Jiayu,Li Shiyu,Wu Di,Jin Shuaiqi. Optimizing Automatic Question Answering System Based on Disease Knowledge Graph[J]. 数据分析与知识发现, 2021, 5(5): 115-126.
[5] Dai Bing,Hu Zhengyin. Review of Studies on Literature-Based Discovery[J]. 数据分析与知识发现, 2021, 5(4): 1-12.
[6] Shi Xiang,Liu Ping. Extraction and Representation of Domain Knowledge with Semantic Description Model and Knowledge Elements——Case Study of Information Retrieval[J]. 数据分析与知识发现, 2021, 5(4): 123-133.
[7] Sheng Shu, Huang Qi, Yang Yang, Xie Qiwen, Qin Xinguo. Exchanging Chinese Medical Information Based on HL7 FHIR[J]. 数据分析与知识发现, 2021, 5(11): 13-28.
[8] Yu Chuanming, Zhang Zhengang, Kong Lingge. Comparing Knowledge Graph Representation Models for Link Prediction[J]. 数据分析与知识发现, 2021, 5(11): 29-44.
[9] Zeng Zhen,Li Gang,Mao Jin,Chen Jinghao. Data Governance and Domain Ontology of Regional Public Security[J]. 数据分析与知识发现, 2020, 4(9): 41-55.
[10] Liang Ye,Li Xiaoyuan,Xu Hang,Hu Yiran. CLOpin: A Cross-Lingual Knowledge Graph Framework for Public Opinion Analysis and Early Warning[J]. 数据分析与知识发现, 2020, 4(6): 1-14.
[11] Lv Huakui,Hong Liang,Ma Feicheng. Constructing Knowledge Graph for Financial Equities[J]. 数据分析与知识发现, 2020, 4(5): 27-37.
[12] Sun Xinrui,Meng Yu,Wang Wenle. Identifying Traffic Events from Weibo with Knowledge Graph and Target Detection[J]. 数据分析与知识发现, 2020, 4(12): 136-147.
[13] Zhu Chaoyu, Liu Lei. A Review of Medical Decision Supports Based on Knowledge Graph[J]. 数据分析与知识发现, 2020, 4(12): 26-32.
[14] Hu Zhengyin,Liu Leilei,Dai Bing,Qin Xiaochu. Discovering Subject Knowledge in Life and Medical Sciences with Knowledge Graph[J]. 数据分析与知识发现, 2020, 4(11): 1-14.
[15] Wang Yi,Shen Zhe,Yao Yifan,Cheng Ying. Domain-Specific Event Graph Construction Methods:A Review[J]. 数据分析与知识发现, 2020, 4(10): 1-13.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938