Application of an ontology for model cards to generate computable artifacts for linking machine learning information from biomedical research

Model card reports provide a transparent description of machine learning models which includes information about their evaluation, limitations, intended use, etc. Federal health agencies have expressed an interest in model cards report for research studies using machine-learning based AI. Previously...

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Veröffentlicht in:Proceedings of the ... International World-Wide Web Conference. International WWW Conference Jg. 2023; H. Companion; S. 820
Hauptverfasser: Amith, Muhammad Tuan, Cui, Licong, Roberts, Kirk, Tao, Cui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Netherlands 01.04.2023
Schlagworte:
description logic
FAIR
document engineering
inference
semantic web
model card reports
transparency
machine learning
artificial intelligence
ontology
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Zusammenfassung:Model card reports provide a transparent description of machine learning models which includes information about their evaluation, limitations, intended use, etc. Federal health agencies have expressed an interest in model cards report for research studies using machine-learning based AI. Previously, we have developed an ontology model for model card reports to structure and formalize these reports. In this paper, we demonstrate a Java-based library (OWL API, FaCT++) that leverages our ontology to publish computable model card reports. We discuss future directions and other use cases that highlight applicability and feasibility of ontology-driven systems to support FAIR challenges.
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DOI:10.1145/3543873.3587601