Towards models of conceptual and procedural operator knowledge

To increase the utility of semantic industrial information models we propose a methodology to incorporate extracted operator knowledge, which we assume to be present in the form of rules, in knowledge graphs. To this end, we present multiple modelling patterns that can be combined depending on the required complexity. Aiming to combine information models with learning systems we contemplate desired behaviours of embeddings from a predictive quality perspective and provide a suited embedding methodology. This methodology is evaluated on a real world dataset of a fused deposition modelling process.

Metadaten
Author:	Richard Nordsieck, Michael Heider ORCiD GND, Anton Hummel, Alwin Hoffmann ORCiD GND, Jörg Hähner ORCiD GND
URN:	urn:nbn:de:bvb:384-opus4-1040087
Frontdoor URL	https://opus.bibliothek.uni-augsburg.de/opus4/104008
URL:	https://ceur-ws.org/Vol-3355/
ISSN:	1613-0073OPAC
Parent Title (English):	SemIIM 2022 - International Workshop on Semantic Industrial Information Modelling 2022: Proceedings of the First International Workshop on Semantic Industrial Information Modelling (SemIIM 2022) co-located with the 19th Extended Semantic Web Conference ESWC 2022, Greece, Crete, 30 May 2022
Publisher:	CEUR-WS
Place of publication:	Aachen
Editor:	Arild Waaler, Evgeny Kharlamov, Baifan Zhou, Dongzhuoran Zhou
Type:	Conference Proceeding
Language:	English
Year of first Publication:	2022
Publishing Institution:	Universität Augsburg
Release Date:	2023/04/25
Series:	CEUR Workshop Proceedings ; 3355
Institutes:	Fakultät für Angewandte Informatik
	Fakultät für Angewandte Informatik / Institut für Informatik
	Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Organic Computing
Dewey Decimal Classification:	0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):	CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)

Open Access