The landscape of ontologies in materials science and engineering: A survey and evaluation

TL;DR

This paper surveys the landscape of ontologies in Materials Science and Engineering to address the lack of comprehensive quality-control analyses. It combines expert insights from the Platform Material Digital program with a formal metric framework—encompassing base, schema, and graph metrics—and uses OOPS! to detect pitfalls, evaluating 60 ontologies (out of 94 artifacts). Key findings show widespread reuse of foundational ontologies such as BFO and EMMO, limited publication of competency questions, and recurring ontology pitfalls, signaling challenges for interoperability and maintainability. The work provides a decision-support resource for domain experts selecting ontologies and outlines future directions to improve ontology design quality and FAIRness in MSE.

Abstract

Ontologies are widely used in materials science to describe experiments, processes, material properties, and experimental and computational workflows. Numerous online platforms are available for accessing and sharing ontologies in Materials Science and Engineering (MSE). Additionally, several surveys of these ontologies have been conducted. However, these studies often lack comprehensive analysis and quality control metrics. This paper provides an overview of ontologies used in Materials Science and Engineering to assist domain experts in selecting the most suitable ontology for a given purpose. Sixty selected ontologies are analyzed and compared based on the requirements outlined in this paper. Statistical data on ontology reuse and key metrics are also presented. The evaluation results provide valuable insights into the strengths and weaknesses of the investigated MSE ontologies. This enables domain experts to select suitable ontologies and to incorporate relevant terms from existing resources.