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Towards evolutionary knowledge representation under the big data circumstance

Xuhui Li (School of Information Management, Wuhan University, Wuhan, China)
Liuyan Liu (School of Information Management, Wuhan University, Wuhan, China)
Xiaoguang Wang (Big Data Institute, Wuhan University, Wuhan, China)
Yiwen Li (School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China)
Qingfeng Wu (School of Information Management, Wuhan University, Wuhan, China)
Tieyun Qian (School of Computer Science, Wuhan University, Wuhan, China)

The Electronic Library

ISSN: 0264-0473

Article publication date: 5 July 2021

Issue publication date: 4 November 2021




The purpose of this paper is to propose a graph-based representation approach for evolutionary knowledge under the big data circumstance, aiming to gradually build conceptual models from data.


A semantic data model named meaning graph (MGraph) is introduced to represent knowledge concepts to organize the knowledge instances in a graph-based knowledge base. MGraph uses directed acyclic graph–like types as concept schemas to specify the structural features of knowledge with intention variety. It also proposes several specialization mechanisms to enable knowledge evolution. Based on MGraph, a paradigm is introduced to model the evolutionary concept schemas, and a scenario on video semantics modeling is introduced in detail.


MGraph is fit for the evolution features of representing knowledge from big data and lays the foundation for building a knowledge base under the big data circumstance.


The representation approach based on MGraph can effectively and coherently address the major issues of evolutionary knowledge from big data. The new approach is promising in building a big knowledge base.



This research is supported by the NSF of China under contract No. 91646206, No.71874129 and No.71921002 and the NSF of Hubei Province under contract No. 2019CFA025; it is also supported by National Demonstration Center for Experimental Library and Information Science Education, Wuhan University.


Li, X., Liu, L., Wang, X., Li, Y., Wu, Q. and Qian, T. (2021), "Towards evolutionary knowledge representation under the big data circumstance", The Electronic Library, Vol. 39 No. 3, pp. 392-410.



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