Ontologies have the potential to be widely re-used in the domain of materials science for the purpose of describing experiments, processes, properties of materials, and experimental and computational workflows [1, 2]. There are online repositories and portals that offer access to these MSE ontologies including MatPortal [3], BioPortal [4], and IndustryPortal [5] among others. However, the evaluation of these ontologies with respect to quality-control metrics, such as accuracy, completeness, and consistency, is missing. Evaluating ontologies against established quality metrics is crucial for ensuring their effectiveness, reducing potential errors, and enhancing their re-use. In addition, the metadata information provided for ontologies in many repositories is often insufficient, which hinders the understanding of the ontology's scope and domain, making it challenging for MSE domain experts to assess its relevance to their specific requirements. This work presents an overview of ontologies used in Materials Science and Engineering in order to guide the domain experts to decide which of the already available ontologies would be best suited for a particular purpose. We have identified 55 ontologies that are evaluated and assessed by adopting the natural language processing (NLP) techniques like term frequency–inverse document frequency (TF-IDF) [6] and N-Grams. Our analysis also provides information about whether the ontologies investigated are actively and openly maintained. The evaluation results offer valuable insights into the strengths and weaknesses of the MSE ontologies under investigation. Domain experts can leverage this information to identify the appropriate ontologies, and also to import and reuse terms from existing ontologies.


References
[1] Zhang, X., Zhao, C., & Wang, X. (2015), A survey on knowledge representation in materials science and engineering: An ontological perspective. Computers in Industry, 73, 8-22.
[2] Bayerlein, B., Hanke, T., Muth, T., Riedel, J., Schilling, M., Schweizer, C., Skrotzki, B., Todor, A., Moreno Torres, B., Unger, J.F., Völker, C. and Olbricht, J. (2022), A Perspective on Digital Knowledge Representation in Materials Science and Engineering. Adv. Eng. Mater., 24: 2101176.
[3] https://matportal.org/
[4] https://bioportal.bioontology.org/
[5] http://industryportal.enit.fr/
[6] Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information processing & management, 24(5), 513-523.