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
[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.
王颖,钱力,谢靖,常志军,孔贝贝. 科技大数据知识图谱构建模型与方法研究*[J]. 数据分析与知识发现, 2019, 3(1): 15-26.
Ying Wang,Li Qian,Jing Xie,Zhijun Chang,Beibei Kong. Building Knowledge Graph with Sci-Tech Big Data. Data Analysis and Knowledge Discovery, DOI：10.11925/infotech.2096-3467.2018.1354.
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