Towards evolutionary knowledge representation under the big data circumstance

Purpose 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 rep...

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Veröffentlicht in:Electronic library Jg. 39; H. 3; S. 392 - 410
Hauptverfasser: Li, Xuhui, Liu, Liuyan, Wang, Xiaoguang, Li, Yiwen, Wu, Qingfeng, Qian, Tieyun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Emerald Publishing Limited 04.11.2021
Emerald Group Publishing Limited
Schlagworte:
Big Data
Cognition & reasoning
Cognitive Structures
Conceptual models
Data models
Discovery tools
Electronic Libraries
Engineering
Evolution
Graphical representations
Graphs
Knowledge
Knowledge base
Knowledge bases (artificial intelligence)
Knowledge discovery
Knowledge management
Knowledge organization
Knowledge representation
Logical Thinking
Networks
Ontology
Pattern Recognition
Representation
Researchers
Resource Description Framework-RDF
Schemas
Semantic web
Semantics
Semiotics
Software engineering
Specialization
Teaching Methods
Video Technology
Web Ontology Language-OWL
Modeling
Big data
Knowledge representation
ISSN:0264-0473, 1758-616X
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Abstract Purpose 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.
AbstractList Purpose 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.
Purpose>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.
Author Wang, Xiaoguang
Wu, Qingfeng
Qian, Tieyun
Li, Xuhui
Li, Yiwen
Liu, Liuyan
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  fullname: Qian, Tieyun
  email: qty@whu.edu.cn
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10.1016/j.websem.2003.07.001
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10.1145/230538.230540
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SubjectTerms Big Data
Cognition & reasoning
Cognitive Structures
Conceptual models
Data models
Discovery tools
Electronic Libraries
Engineering
Evolution
Graphical representations
Graphs
Knowledge
Knowledge base
Knowledge bases (artificial intelligence)
Knowledge discovery
Knowledge management
Knowledge organization
Knowledge representation
Logical Thinking
Networks
Ontology
Pattern Recognition
Representation
Researchers
Resource Description Framework-RDF
Schemas
Semantic web
Semantics
Semiotics
Software engineering
Specialization
Teaching Methods
Video Technology
Web Ontology Language-OWL
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