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Erschienen in:
Applying Semantic Web Technologies to Assess Maintenance Tasks from Operational Interruptions: A Use-Case at Airbus Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Enhancing White-Box Machine Learning Processes by Incorporating Semantic Background Knowledge

verfasst von : Gilles Vandewiele

Erschienen in: The Semantic Web

Verlag: Springer International Publishing

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Abstract

Currently, most of white-box machine learning techniques are purely data-driven and ignore prior background and expert knowledge. A lot of this knowledge has already been captured in domain models, i.e. ontologies, using Semantic Web technologies. The goal of this research proposal is to enhance the predictive performance and required training time of white-box models by incorporating the vast amount of available knowledge in the pre-processing, feature extraction and selection phase of a machine learning process.

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Vorheriges Kapitel Towards an Open Extensible Framework for Empirical Benchmarking of Data Management Solutions: LITMUS
Fußnoten
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Metadaten
Titel
Enhancing White-Box Machine Learning Processes by Incorporating Semantic Background Knowledge
verfasst von
Gilles Vandewiele
Copyright-Jahr
2017
Buch
The Semantic Web

Print ISBN: 978-3-319-58450-8

Electronic ISBN: 978-3-319-58451-5

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-58451-5_21

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