To read this content please select one of the options below:

From data to knowledge: the relationships between vocabularies, linked data and knowledge graphs

Junzhi Jia (School of Information Resource Management, Renmin University of China, Beijing, China)

Journal of Documentation

ISSN: 0022-0418

Article publication date: 4 August 2020

Issue publication date: 24 December 2020




The purpose of this paper is to identify the concepts, component parts and relationships between vocabularies, linked data and knowledge graphs (KGs) from the perspectives of data and knowledge transitions.


This paper uses conceptual analysis methods. This study focuses on distinguishing concepts and analyzing composition and intercorrelations to explore data and knowledge transitions.


Vocabularies are the cornerstone for accurately building understanding of the meaning of data. Vocabularies provide for a data-sharing model and play an important role in supporting the semantic expression of linked data and defining the schema layer; they are also used for entity recognition, alignment and linkage for KGs. KGs, which consist of a schema layer and a data layer, are presented as cubes that organically combine vocabularies, linked data and big data.


This paper first describes the composition of vocabularies, linked data and KGs. More importantly, this paper innovatively analyzes and summarizes the interrelatedness of these factors, which comes from frequent interactions between data and knowledge. The three factors empower each other and can ultimately empower the Semantic Web.



This research has been made possible through the financial support of the National Social Science Foundation of China under Grants No. 19BTQ023, which is “Research on Vocabularies Reusing in the Open Data” project. The author wishes to thank members of the team.


Jia, J. (2021), "From data to knowledge: the relationships between vocabularies, linked data and knowledge graphs", Journal of Documentation, Vol. 77 No. 1, pp. 93-105.



Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

Related articles