数据与计算发展前沿 ›› 2019, Vol. 1 ›› Issue (1): 82-93.doi: 10.11871/jfdc.issn.2096.742X.2019.01.009

所属专题: “数据与计算平台”专刊

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SKS:一种科技领域大数据知识图谱平台 *

周园春1,2,常青玲1,杜一1   

  1. 1.中国科学院计算机网络信息中心,大数据技术与应用发展部,北京100190
    2. 中国科学院大学,北京 100049
  • 收稿日期:2019-08-15 出版日期:2019-01-20 发布日期:2019-10-09
  • 作者简介:周园春,1975年生,中国科学院计算机网络信息中心,博士,研究员,博士生导师,中科院特聘研究员,中心主任助理,中心学位评定委员会主席,大数据技术与应用发展部主任,大数据分析与计算技术国家地方联合工程实验室秘书长,国家烟草专卖局烟草科研大数据重大专项技术首席。发表SCI/EI收录论文90多篇。主要研究方向为云计算、大数据分析与处理。
    本文主要承担工作为SKS整体架构设计及应用项目整体框架设计。
    Zhou Yuanchun was born in 1975. In 2006, he received his Ph.D. from the Institute of Computing Technology, Chinese Academy of Sciences. He is the research fellow, Ph.D. supervisor and the assistant Director in Computer Network Information Center, Chinese Academy of Sciences and the Director of the Department of Big Data Technology and Application Development. He is also the Chairman of the Degree Evaluation Committee in Computer Network Information Center, Chinese Academy of Sciences. His research interests include cloud computing, big data analysis and processing. He has published more than 90 papers included in SCI/EI.
    In this paper he is mainly responsible for the overall framework design of SKS.
    E-mail: zyc@cnic.cn|杜一,1988年生,中国科学院计算机网络信息中心,博士,副研究员,硕士研究生导师,大数据技术与应用发展部大数据应用服务技术实验室主任,主要研究方向大数据管理与处理技术、大数据挖掘与分析技术、科学大数据技术。发表SCI/EI收录论文30多篇。
    本文主要承担文献调研及平台概述。
    Du Yi was born in 1988. He received his Ph.D. in Institute of Software, Chinese Academy of Sciences in 2013. He is an associate professor at the Department of Big Data Technology and Application Development at Computer Network Information Center, Chinese Academy of Sciences. His research interests include big data management, processing, mining, analysis and other related technologies. And he has published over 30 papers included in SCI/EI.
    In this paper he is mainly responsible for literature research and platform overview.
    E-mail: duyi@cnic.cn
  • 基金资助:
    *国家重点研发计划云计算与大数据重点专项(2018YFB1004001);国家自然科学基金重点项目(61836013);中国烟草总公司科技重大专项(110201901027SJ-06)

SKS: A Platform for Big Data Based Scientific Knowledge Graph

Yuanchun Zhou1,2,Qingling Chang1,Yi Du1   

  1. 1.Department of Big Data Technology and Application Development, Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2019-08-15 Online:2019-01-20 Published:2019-10-09

摘要:

【目的】科技领域大数据知识图谱致力于为科研工作者提供更精准、更全面、更有深度和广度的检索与分析结果,进而为学科研究提供切实的、有价值的参考。【文献范围】文章重点调研国内外基于数据的科技评估方法、基于知识图谱的交叉学科研究,以及知识图谱构建中的关键技术方法和基于领域知识的知识图谱建设应用等。【方法】本文给出一种科技领域大数据知识图谱平台SKS,基于SKS平台的整体架构,阐述构建科技领域知识图谱的关键技术及平台工具,并给出相关关键技术及平台在不同领域的应用。【结果】SKS平台及应用在为相关领域构建资源知识管理系统的同时,为科研人员提供了精准的、多维的、相互关联的智能检索服务。【局限】科技领域大数据知识图谱在不断发展中,数据质量(数据源自身质量及数据融合产生的误差)在一定程度上影响了平台的应用效果,未来希望在数据消歧方面进行更多的探索。【结论】科技领域大数据知识图谱以其较强的语义处理能力和关系发掘能力,较好的组织了科技领域的人员、机构、成果、事件等海量异质异构数据,为科技评估提供辅助功能,其在具体项目中的应用效果均获得领域专家的认可。

关键词: 知识图谱, 大数据, 数据采集, 汇聚融合, 关联关系, 可视化

Abstract:

[Objective] The big data knowledge graph in the field of science and technology is dedicated to providing researchers with more accurate, comprehensive, deeper and broader search and analysis results, and thus providing a practical and valuable reference for disciplinary research. [Scope of the literature] The article focuses on the research of data-based scientific and technological evaluation methods at home and abroad, the interdisciplinary research based on knowledge graph, the key technical methods in the construction of knowledge graph and the application of knowledge graph based on domain knowledge. [Methods] This paper presents a large-data knowledge graph platform SKS in the field of science and technology. Based on the overall architecture of the SKS platform, we expound the key technologies and platform tools for constructing knowledge graph in the field of science and technology, and gives relevant key technologies and applications in different fields. [Results] The SKS platform and application provide a precise, multi-dimensional and interrelated intelligent retrieval service for researchers while constructing a resource knowledge management system for related fields. [Limitations] The big data knowledge graph in the field of science and technology is constantly developing. The data quality (the error caused by the data fusion and the quality of source data) affects the application effect of the platform to a certain extent. In the future, we hope to carry out more in data disambiguation. [Conclusions] The big data knowledge graph in the field of science and technology, with its strong semantic processing ability and relationship exploration ability, can organize massive data of personnel, institutions, achievements, events, etc. in the field of science and technology in a better way, which provides auxiliary functions for technology evaluation. The application effects in specific projects are recognized by corresponding domain experts.

Key words: Knowledge graph, Big data, Data collection, Convergence and merge, Linkage, Visualization