Towards a knowledge structuring framework for decision making within industry 4.0 paradigm

Meski, Oussama; Belkadi, Farouk; Furet, Benoît; Laroche, Florent

doi:10.1016/j.ifacol.2019.11.128

Cited by 2 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first one is case-driven and defines structures to gather data directly on a specific domain for restricted applications. Meski et al [34] define a knowledge structuring framework and propose improving applications supported by it. At the highest level of abstraction of this previous framework, authors detail a metamodel that contains concepts such as Process, People, Product or Resource , to name a few.…”

Section: Related Workmentioning

confidence: 99%

A generic metamodel for data extraction and generic ontology population

Chasseray

Barthe‐Delanoë

Negny

et al. 2021

Journal of Information Science

View full text Add to dashboard Cite

As the next step in the development of intelligent computing systems is the addition of human expertise and knowledge, it is a priority to build strong computable and well-documented knowledge bases. Ontologies partially respond to this challenge by providing formalisms for knowledge representation. However, one major remaining task is the population of these ontologies with concrete application. Based on Model-Driven Engineering principles, a generic metamodel for the extraction of heterogeneous data is presented in this article. The metamodel has been designed with two objectives, namely (1) the need of genericity regarding the source of collected pieces of knowledge and (2) the intent to stick to a structure close to an ontological structure. As well, an example of instantiation of the metamodel for textual data in chemistry domain and an insight of how this metamodel could be integrated in a larger automated domain independent ontology population framework are given.

show abstract

Section: Related Workmentioning

confidence: 99%

A generic metamodel for data extraction and generic ontology population

Chasseray

Barthe‐Delanoë

Negny

et al. 2021

Journal of Information Science

View full text Add to dashboard Cite

show abstract

“…The first one includes raw and smart data (separated in three databases) while the second gathers all the available industry knowledge, the business rules, the set of KPIs and reports generated through the reporting processes. Thus, the four knowledge types distinguished in this work are raw data base, smart data base, traceability data base and knowledge repository (Meski et al 2019a).…”

Section: The Analysis Phasementioning

confidence: 99%

A generic knowledge management approach towards the development of a decision support system

Meski

Belkadi

Laroche

et al. 2020

International Journal of Production Research

View full text Add to dashboard Cite

The fourth industrial revolution is increasingly implemented in most fields and categories of industry. The use of several highly developed technologies generates huge amounts of heterogeneous data and knowledge, hence the need to manage it in order to facilitate its reuse. This article proposes a global approach of knowledge management, from the analysis and development of the knowledge base structuring models, to the implementation phase using knowledge engineering tools. This research work is part of a global knowledge-based decision support framework that ensures several axes of decision support, this article deals with the diagnosis axis. It provides analysis and comprehension of the failures occurring during production. The approach has been implemented as a first demonstrator tested in a real case study: the aeronautical mechanical machining industry. This article details the issues of this implementation and its interest for the validation of the approach.

show abstract

Towards a knowledge structuring framework for decision making within industry 4.0 paradigm

Cited by 2 publications

References 11 publications

A generic metamodel for data extraction and generic ontology population

A generic metamodel for data extraction and generic ontology population

A generic knowledge management approach towards the development of a decision support system

Contact Info

Product

Resources

About