Application of An Ontology For Model Cards To Generate Computable Artifacts For Linking Machine Learning Information From Biomedical Research

Authors

Muhammad Tuan Amith
Licong Cui
Kirk Roberts
Cui Tao

Publication Date

4-1-2023

Journal

Proceedings of the 24th International Conference on World Wide Web

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

Comments

PMID: 38327770

DOI

10.1145/3543873.3587601

PMID

38327770

PMCID

PMC10848146

PubMedCentral® Posted Date

February 2024

PubMedCentral® Full Text Version

Author MSS