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  4. A Methodology for Integrating Hierarchical VMAP-Data Structures into an Ontology Using Semantically Represented Analyses
 
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2024
Journal Article
Title

A Methodology for Integrating Hierarchical VMAP-Data Structures into an Ontology Using Semantically Represented Analyses

Abstract
Integrating physical simulation data into data ecosystems challenges the compatibility and interoperability of data management tools. Semantic web technologies and relational databases mostly use other data types, such as measurement or manufacturing design data. Standardizing simulation data storage and harmonizing the data structures with other domains is still a challenge, as current standards such as the ISO standard STEP (ISO 10303 "Standard for the Exchange of Product model data") fail to bridge the gap between design and simulation data. This challenge requires new methods, such as ontologies, to rethink simulation results integration. This research describes a new software architecture and application methodology based on the industrial standard "Virtual Material Modelling in Manufacturing" (VMAP). The architecture integrates large quantities of structured simulation data and their analyses into a semantic data structure. It is capable of providing data permeability from the global digital twin level to the detailed numerical values of data entries and even new key indicators in a three-step approach: It represents a file as an instance in a knowledge graph, queries the file’s metadata, and finds a semantically represented process that enables new metadata to be created and instantiated.
Author(s)
Spelten, Philipp Christoph  orcid-logo
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Meyer, Morten-Christian
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Wagner, Anna
Wolf, Klaus  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Reith, Dirk  orcid-logo
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Journal
Information  
Open Access
DOI
10.3390/info15010021
Language
English
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
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