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

Bielski, Pawel ORCID iD icon 1; Witterauf, Lena 2; Jendral, Sönke 2; Mikut, Ralf ORCID iD icon 3; Bach, Jakob ORCID iD icon 1
1 Institut für Programmstrukturen und Datenorganisation (IPD), Karlsruher Institut für Technologie (KIT)
2 Karlsruher Institut für Technologie (KIT)
3 Institut für Automation und angewandte Informatik (IAI), Karlsruher Institut für Technologie (KIT)

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 datase ts. We also explore the potential root causes of these mismatches and discuss strategies to address them.

Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Institut für Programmstrukturen und Datenorganisation (IPD)
Publikationstyp Proceedingsbeitrag
Publikationsmonat/-jahr 11.2024
Sprache Englisch
Identifikator ISBN: 978-989-758-716-0
ISSN: 2184-3228
KITopen-ID: 1000176553
HGF-Programm 47.14.02 (POF IV, LK 01) Information Storage and Processing in the Cell Nucleus
Erschienen in Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2024). Ed.: D. Aveiro. Vol. 2
Veranstaltung 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2024), Porto, Portugal, 17.11.2024 – 19.11.2024
Verlag SciTePress
Seiten 216–226
Schlagwörter Ontology Quality Evaluation, Knowledge-Guided Machine Learning, Application Ontology
Nachgewiesen in Dimensions
OpenAlex
Scopus
Relationen in KITopen

Verlagsausgabe §
DOI: 10.5445/IR/1000176553
Veröffentlicht am 03.02.2025
Seitenaufrufe: 58
seit 23.11.2024
Downloads: 14
seit 05.02.2025
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