Quantifying Domain-Application Knowledge Mismatch in Ontology-Guided Machine Learning

Pawel Bielski, Lena Witterauf, Sönke Jendral, Ralf Mikut, Jakob Bach

2024

Abstract

In this work, we study the critical issue of knowledge mismatch in ontology-guided machine learning (OGML), specifically between domain ontologies and application ontologies. Such mismatches may arise when OGML uses ontological knowledge that was originally created for different purposes. Even if ontological knowledge improves the overall OGML performance, mismatches can lead to reduced performance on specific data subsets compared to machine-learning models without ontological knowledge. We propose a framework to quantify this mismatch and identify the specific parts of the ontology that contribute to it. To demonstrate the framework’s effectiveness, we apply it to two common OGML application areas: image classification and patient health prediction. Our findings reveal that domain-application mismatches are widespread across various OGML approaches, machine-learning model architectures, datasets, and prediction tasks, and can impact up to 40% of unique domain concepts in the datasets. We also explore the potential root causes of these mismatches and discuss strategies to address them.

Paper Citation

in Harvard Style

Bielski P., Witterauf L., Jendral S., Mikut R. and Bach J. (2024). Quantifying Domain-Application Knowledge Mismatch in Ontology-Guided Machine Learning. In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD; ISBN 978-989-758-716-0, SciTePress, pages 216-226. DOI: 10.5220/0013065900003838

in Bibtex Style

@conference{keod24,
author={Pawel Bielski and Lena Witterauf and Sönke Jendral and Ralf Mikut and Jakob Bach},
title={Quantifying Domain-Application Knowledge Mismatch in Ontology-Guided Machine Learning},
booktitle={Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD},
year={2024},
pages={216-226},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013065900003838},
isbn={978-989-758-716-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD
TI - Quantifying Domain-Application Knowledge Mismatch in Ontology-Guided Machine Learning
SN - 978-989-758-716-0
AU - Bielski P.
AU - Witterauf L.
AU - Jendral S.
AU - Mikut R.
AU - Bach J.
PY - 2024
SP - 216
EP - 226
DO - 10.5220/0013065900003838
PB - SciTePress