Reliable and interoperable computational molecular engineering: 2. Semantic interoperability based on the European Materials and Modelling Ontology
Martin Thomas Horsch, Silvia Chiacchiera, Youness Bami, Georg J., Schmitz, Gabriele Mogni, Gerhard Goldbeck, and Emanuele Ghedini

TL;DR
This paper discusses the development of the European Materials and Modelling Ontology (EMMO) to enable semantic interoperability across various computational materials engineering platforms and the integration of domain ontologies within a European Virtual Marketplace Framework.
Contribution
It introduces the EMMO as a top-level ontology and explores ontology alignment challenges for integrating domain-specific ontologies into a unified marketplace framework.
Findings
EMMO facilitates semantic interoperability in materials modelling.
Ontology alignment is crucial for integrating diverse domain ontologies.
The framework supports a European Virtual Marketplace for materials modelling tools.
Abstract
The European Materials and Modelling Ontology (EMMO) is a top-level ontology designed by the European Materials Modelling Council to facilitate semantic interoperability between platforms, models, and tools in computational molecular engineering, integrated computational materials engineering, and related applications of materials modelling and characterization. Additionally, domain ontologies exist based on data technology developments from specific platforms. The present work discusses the ongoing work on establishing a European Virtual Marketplace Framework, into which diverse platforms can be integrated. It addresses common challenges that arise when marketplace-level domain ontologies are combined with a top-level ontology like the EMMO by ontology alignment.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSemantic Web and Ontologies · Machine Learning in Materials Science · Scientific Computing and Data Management
