Elementary Multiperspective Material Ontology paper out now

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Elementary Multiperspective Material Ontology: Leveraging
Perspectives via a Showcase of EMMO-Based Domain
and Application Ontologies

 

An article outlining the Elementary Multiperspective Material Ontology (EMMO) has been published in the Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. This article was written by Goldbeck Consulting, along with colleagues from SINTEF, Norway, and the Univeristy of Bologna, Italy, as part of the NanoMECommons and OpenModel EU projects.

This image shows EMMO’s architecture, with its backbone based on mereocausality and its implementation of physics, chemistry
and materials, grounded in current natural science foundations (StandardModel). This figure was taken from Del Nostro, P. et al. Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management – KEOD, 135–142 (2024) published under a CC BY-NC-ND 4.0 licence.

The paper outlines the foundations of EMMO and describes its pluralistic (multi-perspective) approach. The paper goes on to describe three application ontologies built on the EMMO: a Battery Testing Ontology, which provides a structured framework for representing knowledge related to battery testing and quality control; a Hyperdimensional Polymer Ontology, which is designed to represent the wide variety of polymeric materials focusing on manufacturing aspects; and a MarketPlace Agent and Expert Ontology, which models experts, their expertise, and the community in the field of Materials Modeling.

Elementary Multiperspective Material Ontology: Leveraging Perspectives via a Showcase of EMMO-Based Domain and Application Ontologies

was written by Pierluigi Del Nostro, Gerhard Goldbeck, Ferry Kienberger, Manuel Moertelmaier, Andrea Pozzi, Nawfal Al-Zubaidi-R-Smith, and Daniele Toti.

EMMOpaper

Or to view the paper, click here.

A version of record of this work is available here.

 

Acknowledgement: This work has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreements No 952869 (NanoMECommons) and No 953167 (OpenModel)