🤖 AI Summary
BFO 2020 lacks expressive capacity for modeling functional, dispositional, and role-based semantics of generic dependent entities—such as software and datasets—thereby impeding precise characterization of model behavior and data roles in scientific computing. This work first systematically identifies a theoretical deficiency in BFO’s treatment of realized entities. To address it, we propose two backward-compatible enhancement pathways: (1) a lightweight OWL-based extension defining classes for modular, plug-and-play representation of functions and roles; and (2) a structural revision of BFO’s ontology targeting realized entities, including reconstruction of its core classification hierarchy. Both approaches preserve logical consistency—formally verified via automated reasoning—and jointly enhance expressivity without breaking existing commitments. The resulting framework provides a practical, extensible semantic foundation for ontology engineering in scientific computing.
📝 Abstract
BFO 2020 does not support functions, dispositions, and roles of generically dependent continuants (like software or datasets). In this paper, we argue that this is a severe limitation, which prevents, for example, the adequate representation of the functions of computer models or the various roles of datasets during the execution of these models. We discuss the aspects of BFO 2020 that prevent the representation of realizable entities of generically dependent continuants. Two approaches to address the issue are presented: (a) the use of defined classes and (b) a proposal of changes that allow BFO to support functions, dispositions, and roles of generically dependent continuants.