In:
The Electronic Library, Emerald, Vol. 39, No. 3 ( 2021-11-04), p. 392-410
Abstract:
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. Design/methodology/approach 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. Findings 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. Originality/value 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.
Type of Medium:
Online Resource
ISSN:
0264-0473
,
0264-0473
DOI:
10.1108/EL-11-2020-0318
Language:
English
Publisher:
Emerald
Publication Date:
2021
detail.hit.zdb_id:
790046-6
detail.hit.zdb_id:
1500250-0
SSG:
24,1
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