An ontology-based approach for preprocessing in machine learning

Patricia Centeno Soto; Nour Ramzy; Felix Ocker; Birgit Vogel-Heuser

doi:10.1109/INES52918.2021.9512899

An ontology-based approach for preprocessing in machine learning

Patricia Centeno Soto, Nour Ramzy, Felix Ocker, Birgit Vogel-Heuser

Chair of Automation and Information Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Increasing pressure on internationally operating companies leads to the application of novel technologies, e.g., Machine Learning models. However, Machine Learning algorithms require preprocessing, i.e., data cleaning, which is time consuming and requires domain-specific knowledge. Formalized knowledge bases capture such domain-specific knowledge in a computer-interpretable way and have the potential to reduce manual efforts for this process. This paper presents a framework for semantic preprocessing, which is evaluated at the example of an industrial use case from the semiconductor industry.

Original language	English
Title of host publication	INES 2021 - IEEE 25th International Conference on Intelligent Engineering Systems, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	133-138
Number of pages	6
ISBN (Electronic)	9781665444996
DOIs	https://doi.org/10.1109/INES52918.2021.9512899
State	Published - 7 Jul 2021
Event	25th IEEE International Conference on Intelligent Engineering Systems, INES 2021 - Budapest, Hungary Duration: 7 Jul 2021 → 9 Jul 2021

Publication series

Name	INES 2021 - IEEE 25th International Conference on Intelligent Engineering Systems, Proceedings

Conference

Conference	25th IEEE International Conference on Intelligent Engineering Systems, INES 2021
Country/Territory	Hungary
City	Budapest
Period	7/07/21 → 9/07/21

Keywords

machine learning
semantic preprocessing
semantic web technologies

Access to Document

10.1109/INES52918.2021.9512899

Cite this

Soto, P. C., Ramzy, N., Ocker, F., & Vogel-Heuser, B. (2021). An ontology-based approach for preprocessing in machine learning. In INES 2021 - IEEE 25th International Conference on Intelligent Engineering Systems, Proceedings (pp. 133-138). Article 9512899 (INES 2021 - IEEE 25th International Conference on Intelligent Engineering Systems, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INES52918.2021.9512899

Soto, Patricia Centeno ; Ramzy, Nour ; Ocker, Felix et al. / An ontology-based approach for preprocessing in machine learning. INES 2021 - IEEE 25th International Conference on Intelligent Engineering Systems, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 133-138 (INES 2021 - IEEE 25th International Conference on Intelligent Engineering Systems, Proceedings).

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abstract = "Increasing pressure on internationally operating companies leads to the application of novel technologies, e.g., Machine Learning models. However, Machine Learning algorithms require preprocessing, i.e., data cleaning, which is time consuming and requires domain-specific knowledge. Formalized knowledge bases capture such domain-specific knowledge in a computer-interpretable way and have the potential to reduce manual efforts for this process. This paper presents a framework for semantic preprocessing, which is evaluated at the example of an industrial use case from the semiconductor industry.",

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Soto, PC, Ramzy, N, Ocker, F & Vogel-Heuser, B 2021, An ontology-based approach for preprocessing in machine learning. in INES 2021 - IEEE 25th International Conference on Intelligent Engineering Systems, Proceedings., 9512899, INES 2021 - IEEE 25th International Conference on Intelligent Engineering Systems, Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 133-138, 25th IEEE International Conference on Intelligent Engineering Systems, INES 2021, Budapest, Hungary, 7/07/21. https://doi.org/10.1109/INES52918.2021.9512899

An ontology-based approach for preprocessing in machine learning. / Soto, Patricia Centeno; Ramzy, Nour; Ocker, Felix et al.
INES 2021 - IEEE 25th International Conference on Intelligent Engineering Systems, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. p. 133-138 9512899 (INES 2021 - IEEE 25th International Conference on Intelligent Engineering Systems, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - An ontology-based approach for preprocessing in machine learning

AU - Soto, Patricia Centeno

AU - Ramzy, Nour

AU - Ocker, Felix

AU - Vogel-Heuser, Birgit

PY - 2021/7/7

Y1 - 2021/7/7

N2 - Increasing pressure on internationally operating companies leads to the application of novel technologies, e.g., Machine Learning models. However, Machine Learning algorithms require preprocessing, i.e., data cleaning, which is time consuming and requires domain-specific knowledge. Formalized knowledge bases capture such domain-specific knowledge in a computer-interpretable way and have the potential to reduce manual efforts for this process. This paper presents a framework for semantic preprocessing, which is evaluated at the example of an industrial use case from the semiconductor industry.

AB - Increasing pressure on internationally operating companies leads to the application of novel technologies, e.g., Machine Learning models. However, Machine Learning algorithms require preprocessing, i.e., data cleaning, which is time consuming and requires domain-specific knowledge. Formalized knowledge bases capture such domain-specific knowledge in a computer-interpretable way and have the potential to reduce manual efforts for this process. This paper presents a framework for semantic preprocessing, which is evaluated at the example of an industrial use case from the semiconductor industry.

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KW - semantic web technologies

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DO - 10.1109/INES52918.2021.9512899

M3 - Conference contribution

AN - SCOPUS:85114003452

T3 - INES 2021 - IEEE 25th International Conference on Intelligent Engineering Systems, Proceedings

SP - 133

EP - 138

BT - INES 2021 - IEEE 25th International Conference on Intelligent Engineering Systems, Proceedings

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T2 - 25th IEEE International Conference on Intelligent Engineering Systems, INES 2021

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Soto PC, Ramzy N, Ocker F, Vogel-Heuser B. An ontology-based approach for preprocessing in machine learning. In INES 2021 - IEEE 25th International Conference on Intelligent Engineering Systems, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. p. 133-138. 9512899. (INES 2021 - IEEE 25th International Conference on Intelligent Engineering Systems, Proceedings). doi: 10.1109/INES52918.2021.9512899

An ontology-based approach for preprocessing in machine learning

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Keywords

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