An Industry-Ready Machine Learning Ontology
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Semantic Web and Ontologies · Scientific Computing and Data Management · Biomedical Text Mining and Ontologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science, Darmstadt University of Applied Sciences, Schöfferstr. 3, 64295 Darmstadt, Germany
References (36)
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