Ontology based Information Management for Industrial Applications

Markus Germann, Constantin Rieder, Klaus Scherer

2020

Abstract

For industrial applications, intelligent systems are available, helpful and necessary to support the complex human expert decisions and also the design based construction processes, especially when complex constraints for the process behaviour are given. Normally, such an intelligent support system consists of a knowledge based module, which is responsible for the real assistance power, representing the user specified information, the reasoning explanation part and the logical reasoning process itself. The interview based acquisition and generation of the complex expert knowledge itself is very crucial because of differing correlations between the complex parameters. So, in this project intelligent Wiki based methods are researched and developed for a quality improvement of an ontology based information system concerning electronic 3D print processes.

Paper Citation

in Harvard Style

Germann M., Rieder C. and Scherer K. (2020). Ontology based Information Management for Industrial Applications. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-395-7, pages 773-779. DOI: 10.5220/0009095607730779

in Bibtex Style

@conference{icaart20,
author={Markus Germann and Constantin Rieder and Klaus Scherer},
title={Ontology based Information Management for Industrial Applications},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2020},
pages={773-779},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009095607730779},
isbn={978-989-758-395-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Ontology based Information Management for Industrial Applications
SN - 978-989-758-395-7
AU - Germann M.
AU - Rieder C.
AU - Scherer K.
PY - 2020
SP - 773
EP - 779
DO - 10.5220/0009095607730779