TY - JOUR
T1 - The Problem of Competing Scientific Ontologies in Materials Knowledge Representation
AU - Ivan Petrov
AU - Olga Ivanova
AU - Dmitry Smirnov
JF - Journal of Artificial Intelligence for Materials Science
JO - J. Artif. Intell. Mater. Sci.
SN - 3149-8957
Y1 - 2025
VL - 4
IS - 1
SP - 135
N2 - Competing scientific ontologies represent a pervasive yet under-analyzed failure mode in artificial intelligence applications for materials science. Different classification systems for the same materials, structures, properties, and processes create fundamental incompatibilities that cause AI models to fail in ways that are difficult to diagnose through conventional performance metrics. This paper defines the ontology problem as the inherent challenge of representing material knowledge when multiple, partially incompatible ontologies coexist within the domain, each encoding distinct conceptual boundaries and relational assumptions. It articulates four primary types of ontological competition—category boundary differences, naming conflicts, relationship differences, and granularity differences—that arise repeatedly in materials informatics. These competitions trigger specific failure modes, including transfer failures, evaluation incompatibilities, data integration failures, and communication breakdowns between research communities. Detection relies on explicit ontology audits and cross-ontology testing, while mitigation centers on mapping strategies, ontology-agnostic representations, and community harmonization efforts. By framing ontology competition as a distinct failure mode, the analysis draws on existing literature to propose an ontology-aware framework that strengthens semantic interoperability and model robustness in materials AI. Ultimately, acknowledging and managing competing ontologies is essential for translating data-driven discoveries into reliable, reproducible knowledge.
UR - https://iamrp.net/p844294403
ER -