An Industry-Ready Machine Learning Ontology

This article presents an industry-ready ontology for the machine learning domain, which is named “ML Ontology”. While based on lightweight modelling languages, ML ontology provides novel features including built-in queries and quality assurance, as well as sophisticated reasoning. With ca. 700 individuals that define key ML concepts and ca. 5000 RDF triples, ML Ontology ranks among the largest domain-specific ontologies for ML. An experiment to estimate the correctness and completeness of ML terminology included in ML Ontology indicates an F1-score of 0.83. A benchmark evaluating query performance reveals query response times far below 100 ms even for complex queries and memory consumption below 3.5 MB. Its industry-readiness is demonstrated by benchmarks as well as two use case implementations within a data science platform. ML Ontology is open source and published under an MIT license.

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Publication Details

Journal
Applied Sciences
Published
2026-01-14
DOI
https://doi.org/10.3390/app16020843
Primary Topic
Semantic Web and Ontologies
Type
article
Field-Weighted Citation Impact
0.00
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article

An Industry-Ready Machine Learning Ontology

Bernhard G. Humm
Applied Sciences
Semantic Web and Ontologies
article

An Industry-Ready Machine Learning Ontology

Bernhard G. Humm
article en

Abstract

This article presents an industry-ready ontology for the machine learning domain, which is named “ML Ontology”. While based on lightweight modelling languages, ML ontology provides novel features including built-in queries and quality assurance, as well as sophisticated reasoning. With ca. 700 individuals that define key ML concepts and ca. 5000 RDF triples, ML Ontology ranks among the largest domain-specific ontologies for ML. An experiment to estimate the correctness and completeness of ML terminology included in ML Ontology indicates an F1-score of 0.83. A benchmark evaluating query performance reveals query response times far below 100 ms even for complex queries and memory consumption below 3.5 MB. Its industry-readiness is demonstrated by benchmarks as well as two use case implementations within a data science platform. ML Ontology is open source and published under an MIT license.

Applied SciencesVol. 16(2)
Technische Universität Darmstadt (DE)
Industry, innovation and infrastructure
Openalex Percentile: Top 88%
Semantic Web and Ontologies
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