Constructing Big Data Platform for Sci-Tech Knowledge Discovery with Knowledge Graph
Jiying Hu1,Jing Xie1,2(),Li Qian1,2,Changlei Fu1
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 create a big data platform for sci-tech knowledge discovery, aiming to transform the keyword-based literature retrieval to knowledge retrieval. [Methods] First, we extracted and annotated scientific research entities and calculated their relationship with data mining techniques. Then, we created distributed indexes based on entity knowledge graph, which achieved multi-dimensional knowledge retrieval and correlated navigation. [Results] This study generated knowledge graphs for 10 research entities, such as papers, projects, scholars and institutions, etc. The proposed platform could conduct intelligent semantic search and multi-dimensional knowledge discovery with these knowledge graphs. [Limitations] Our study is at the entity level, and more research is needed for the semantic retrieval. [Conclusions] The proposed platform organizes data at the knowledge level, which meets user’s precise knowledge retrieval demands and improves user experience.
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Jiying Hu,Jing Xie,Li Qian,Changlei Fu. Constructing Big Data Platform for Sci-Tech Knowledge Discovery with Knowledge Graph. Data Analysis and Knowledge Discovery, DOI：10.11925/infotech.2096-3467.2018.1357.
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