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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Published in:Proceedings of the ... International World-Wide Web Conference. International WWW Conference Vol. 2023; no. Companion; p. 820
Main Authors: Amith, Muhammad Tuan, Cui, Licong, Roberts, Kirk, Tao, Cui
Format: Journal Article
Language:English
Published: Netherlands 01.04.2023
Subjects:
description logic
FAIR
document engineering
inference
semantic web
model card reports
transparency
machine learning
artificial intelligence
ontology
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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.
AbstractList 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.
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.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.
Author Cui, Licong
Roberts, Kirk
Tao, Cui
Amith, Muhammad Tuan
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/38327770$$D View this record in MEDLINE/PubMed
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FAIR
document engineering
inference
semantic web
model card reports
transparency
machine learning
artificial intelligence
ontology
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