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Authors: Andreas Bunte 1 ; Peng Li 1 and Oliver Niggemann 2

Affiliations: 1 Institute for Industrial IT, Germany ; 2 Institute for Industrial IT and Fraunhofer IOSB-INA, Germany

ISBN: 978-989-758-275-2

Keyword(s): Clustering, Ontology, Knowledge, Reasoning, Classification, Concept Learning.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Cognitive Systems ; Computational Intelligence ; e-Business ; Enterprise Engineering ; Enterprise Information Systems ; Enterprise Ontologies ; Evolutionary Computing ; Formal Methods ; Industrial Applications of AI ; Knowledge Representation and Reasoning ; Knowledge-Based Systems ; Ontologies ; Simulation and Modeling ; Soft Computing ; Symbolic Systems

Abstract: Machine learning techniques have a huge potential to take some tasks of humans, e.g. anomaly detection or predictive maintenance, and thus support operators of cyber physical systems (CPSs). One challenge is to communicate algorithms results to machines or humans, because they are on a sub-symbolical level and thus hard to interpret. To simplify the communication and thereby the usage of the results, they have to be transferred to a symbolic representation. Today, the transformation is typically static which does not satisfy the needs for fast changing CPSs and prohibit the usage of the full machine learning potential. This work introduces a knowledge based approach of an automatic mapping between the sub-symbolic results of algorithms and their symbolic representation. Clustering is used to detect groups of similar data points which are interpreted as concepts. The information of clusters are extracted and further classified with the help of an ontology which infers the current opera tional state. Data from wind turbines is used to evaluate the approach. The achieved results are promising, the system can identify its operational state without an explicit mapping. (More)

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Paper citation in several formats:
Bunte, A.; Li, P. and Niggemann, O. (2018). Mapping Data Sets to Concepts using Machine Learning and a Knowledge based Approach.In Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-275-2, pages 430-437. DOI: 10.5220/0006590204300437

@conference{icaart18,
author={Andreas Bunte. and Peng Li. and Oliver Niggemann.},
title={Mapping Data Sets to Concepts using Machine Learning and a Knowledge based Approach},
booktitle={Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2018},
pages={430-437},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006590204300437},
isbn={978-989-758-275-2},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Mapping Data Sets to Concepts using Machine Learning and a Knowledge based Approach
SN - 978-989-758-275-2
AU - Bunte, A.
AU - Li, P.
AU - Niggemann, O.
PY - 2018
SP - 430
EP - 437
DO - 10.5220/0006590204300437

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