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Review and Alignment of Domain-Level Ontologies for Materials Science

Anne De Baas, Pierluigi Del Nostro, Jesper Friis, Emanuele Ghedini, Gerhard Goldbeck, Ilaria Maria Paponetti, Andrea Pozzi, Arkopaul Sarkar, Lan Yang, Francesco Antonio Zaccarini, Daniele Toti

2023IEEE Access27 citationsDOIOpen Access PDF

Abstract

The growing complexity and interdisciplinary nature of Materials Science research demand efficient data management and exchange through structured knowledge representation. Domain-Level Ontologies (DLOs) for Materials Science have emerged as a valuable tool for describing materials properties, processes, and structures, enabling effective data integration, interoperability, and knowledge discovery. However, the harmonization of DLOs, and, more generally, the establishment of fully interoperable multi-level ecosystems, remains a challenge due to various factors, including the diverse landscape of existing ontologies. This work provides a comprehensive overview of the state-of-the-art in DLOs for Materials Science, highlighting their main features and purposes. More than 40 DLOs in Materials Science are considered. Furthermore, an alignment methodology including both manual and automated steps, making use of Top-Level Ontologies’ (TLO) capability of promoting interoperability, and revolving around the engineering of FAIR standalone entities acting as minimal data pipelines (“bridge concepts”), is presented. A proof of concept is also provided. The primary aspiration of this undertaking is to make a meaningful contribution towards the establishment of a unified ontology framework for Materials Science, facilitating more effective data integration and fostering interoperability across Materials Science subdomains.

Topics & Concepts

InteroperabilityComputer scienceData scienceOntologyDomain (mathematical analysis)HarmonizationBridge (graph theory)Software engineeringWorld Wide WebMathematicsInternal medicinePhysicsAcousticsEpistemologyPhilosophyMedicineMathematical analysisSemantic Web and OntologiesScientific Computing and Data ManagementBiomedical Text Mining and Ontologies
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