Faculty, Staff and Student Publications
Language
English
Publication Date
4-1-2023
Journal
Proceedings of the 24th International Conference on World Wide Web
DOI
10.1145/3543873.3587601
PMID
38327770
PMCID
PMC10848146
PubMedCentral® Posted Date
February 2024
PubMedCentral® Full Text Version
Author MSS
Abstract
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.
Keywords
model card reports, ontology, semantic web, machine learning, FAIR, transparency, document engineering, inference, description logic, artificial intelligence
Published Open-Access
yes
Recommended Citation
Amith, Muhammad Tuan; Cui, Licong; Roberts, Kirk; et al., "Application of An Ontology For Model Cards To Generate Computable Artifacts For Linking Machine Learning Information From Biomedical Research" (2023). Faculty, Staff and Student Publications. 133.
https://digitalcommons.library.tmc.edu/uthshis_docs/133