Details
| Titel in Übersetzung | Application of machine learning methods in a system based on ontology |
|---|---|
| Originalsprache | Spanisch |
| Titel des Sammelwerks | Application of machine learning methods in a system based on ontology |
| Seiten | 86-97 |
| Seitenumfang | 12 |
| Band | 2096 |
| Publikationsstatus | Veröffentlicht - 7 März 2018 |
| Extern publiziert | Ja |
| Veranstaltung | 3rd International Workshop on Semantic Web, IWSW 2018 - Havana, Kuba Dauer: 5 März 2018 → 9 März 2018 |
Publikationsreihe
| Name | CEUR workshop proceedings |
|---|---|
| Herausgeber (Verlag) | CEUR-WS |
| ISSN (Print) | 1613-0073 |
Abstract
The ontology-based system for the management of environmental indicators in corporations (SIGCIA) allows the detection of an indicator alteration, if it exceeds a limit value. In this case, this system recommends the possible environmental impacts, the causes of the indicator alteration and the mitigation actions. In order to make these recommendations, the limit value for each indicator must be pre-defined in the software by the environmental management specialist. This means that the determination of limit values is done subjectively, based on the knowledge of the historical behavior of the indicator in a specific organization; so it is necessary to have an automatic forecast method. This research transits through all the phases of the process of Knowledge Discovery in Data (KDD). A selection of attributes in the dataset was made applying several selectors and a group of regression models were applied. Artificial Neural Networks with Multi-Layer Perceptron topology showed best performance. It allows the prediction of the limit value of the energy consumption indicator, dataset selected as study case. The prediction of limit values and the potential offered by the ontology-based recommendation system make it a powerful tool to support decision-making in the process of environmental management, with broad generalization possibilities in Cuban business sector.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Allgemeine Computerwissenschaft
Ziele für nachhaltige Entwicklung
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Application of machine learning methods in a system based on ontology. Band 2096 2018. S. 86-97 (CEUR workshop proceedings).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Aplicación de métodos de aprendizaje automático en un sistema basado en ontología
AU - Castellanos, María Isabel
AU - Rivas, Ariam
AU - Lucas, Emilio
N1 - Publisher Copyright: © 2018 CEUR-WS. All rights reserved.
PY - 2018/3/7
Y1 - 2018/3/7
N2 - The ontology-based system for the management of environmental indicators in corporations (SIGCIA) allows the detection of an indicator alteration, if it exceeds a limit value. In this case, this system recommends the possible environmental impacts, the causes of the indicator alteration and the mitigation actions. In order to make these recommendations, the limit value for each indicator must be pre-defined in the software by the environmental management specialist. This means that the determination of limit values is done subjectively, based on the knowledge of the historical behavior of the indicator in a specific organization; so it is necessary to have an automatic forecast method. This research transits through all the phases of the process of Knowledge Discovery in Data (KDD). A selection of attributes in the dataset was made applying several selectors and a group of regression models were applied. Artificial Neural Networks with Multi-Layer Perceptron topology showed best performance. It allows the prediction of the limit value of the energy consumption indicator, dataset selected as study case. The prediction of limit values and the potential offered by the ontology-based recommendation system make it a powerful tool to support decision-making in the process of environmental management, with broad generalization possibilities in Cuban business sector.
AB - The ontology-based system for the management of environmental indicators in corporations (SIGCIA) allows the detection of an indicator alteration, if it exceeds a limit value. In this case, this system recommends the possible environmental impacts, the causes of the indicator alteration and the mitigation actions. In order to make these recommendations, the limit value for each indicator must be pre-defined in the software by the environmental management specialist. This means that the determination of limit values is done subjectively, based on the knowledge of the historical behavior of the indicator in a specific organization; so it is necessary to have an automatic forecast method. This research transits through all the phases of the process of Knowledge Discovery in Data (KDD). A selection of attributes in the dataset was made applying several selectors and a group of regression models were applied. Artificial Neural Networks with Multi-Layer Perceptron topology showed best performance. It allows the prediction of the limit value of the energy consumption indicator, dataset selected as study case. The prediction of limit values and the potential offered by the ontology-based recommendation system make it a powerful tool to support decision-making in the process of environmental management, with broad generalization possibilities in Cuban business sector.
KW - Artificial Neural Networks
KW - Environmental Indicators
KW - Forecast
KW - Ontology-based System
UR - http://www.scopus.com/inward/record.url?scp=85047912455&partnerID=8YFLogxK
M3 - Aufsatz in Konferenzband
AN - SCOPUS:85047912455
VL - 2096
T3 - CEUR workshop proceedings
SP - 86
EP - 97
BT - Application of machine learning methods in a system based on ontology
T2 - 3rd International Workshop on Semantic Web, IWSW 2018
Y2 - 5 March 2018 through 9 March 2018
ER -