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Literature searches identified 47 models for knowledge translation.
Established process models for knowledge discovery find the domain-expert in a customer-like and supervising role.
All the known and relevant process models for knowledge discovery find the (medical) domain-expert in a customer-like, supervising role [3, 42].
In this paper, we revise the established process models for knowledge discovery and propose a new process model for domain-expert-driven interactive knowledge discovery.
The authors present common models for knowledge representation, addressing their statistical characteristics and providing an overview of information retrieval and machine learning methods related to computational linguistics.
In this paper, we elaborate on the systematic design of approaches that combine quantum clustering with intelligent models for knowledge extraction, learning, and representation.
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In that respect, Triple Helix represents a basic model for knowledge production and innovation application.
The model for knowledge management was already proposed in another article.
This paper presents the utility validation of a model for knowledge management.
In that respect, Triple Helix represents a basic model or a core model for knowledge production and innovation application.
We propose a snail shell process model for knowledge discovery via data analytics (KDDA) to address these challenges.
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