Top level ontologies: desirable characteristics in the context of materials science

Pierluigi Del Nostro, Jesper Friis, Emanuele Ghedini, Gerhard Goldbeck, Daniele Toti, Francesco Antonio Zaccarini

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The desiderata for the effective representation of Materials Science and Engineering (MSE) knowledge in Top Level Ontologies (TLOs) are discussed, based on the empirically grounded assumption that different ontologies exhibit different degrees of suitability in different contexts, and with respect to different goals and use-cases. The discussion follows the general requirements for TLOs outlined in ISO/IEC 21838-1, investigating each of them in the context of MSE 's methodological principles and procedural staples. As a result of the analysis, a set of desirable characteristics for TLOs is individuated, providing reasons to favor certain ontology design alternatives. The Elementary Multiperspective Material Ontology (EMMO) is briefly introduced as an example of an ontology engineered to meet the MSE desiderata. While comparing the effectiveness of conceptual frameworks across different contexts remains challenging, the analysis should lead to improvements in knowledge representation for the MSE domain, either directly, or by fostering explicit discussions regarding ontology design choices.
Original languageEnglish
Title of host publicationCEUR Workshop Proceedings
Pages29-40
Number of pages12
Volume3760
Publication statusPublished - 2024
Event1st International Workshop on Semantic Materials Science: Harnessing the Power of Semantic Web Technologies in Materials Science, SeMatS 2024 - Amsterdam, The Netherlands
Duration: 17 Sept 202417 Sept 2024

Conference

Conference1st International Workshop on Semantic Materials Science: Harnessing the Power of Semantic Web Technologies in Materials Science, SeMatS 2024
CityAmsterdam, The Netherlands
Period17/9/2417/9/24

Keywords

  • Knowledge Representation
  • Materials Science and Engineering
  • Science and Industry
  • Top Level Ontology

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