Graphol: A Graphical Language for Ontology Modeling Equivalent to OWL 2

Lembo, Domenico; Santarelli, Valerio; Savo, Domenico Fabio; Giacomo, Giuseppe De

doi:10.3390/fi14030078

Cited by 6 publications

(3 citation statements)

References 49 publications

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“…The ontology for fault diagnosis is defined to enable fault diagnosis based on knowledge. The main environmental data for the smart farm are collected by the sensor network; thus, the ontology is defined based on the semantic sensor network ontology [25][26][27][28][29].…”

Section: Model Designmentioning

confidence: 99%

Artificial Intelligence-Based Fault Diagnosis and Prediction for Smart Farm Information and Communication Technology Equipment

Choe,

Lee

2023

Agriculture

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Despite the recent increase in smart farming practices, system uncertainty and difficulties associated with maintaining farming sites hinder their widespread adoption. Agricultural production systems are extremely sensitive to operational downtime caused by malfunctions because it can damage crops. To resolve this problem, the types of abnormal data, the present error determination techniques for each data type, and the accuracy of anomaly data determination based on spatial understanding of the sensed values are classified in this paper. We design and implement a system to detect and predict abnormal data using a recurrent neural network algorithm and diagnose malfunctions using an ontological technique. The proposed system comprises the cloud in charge of the IoT equipment installed in the farm testbed, communication and control, system management, and a common framework based on machine learning and deep learning for fault diagnosis. It exhibits excellent prediction performance, with a root mean square error of 0.073 for the long short-term memory model. Considering the increasing number of agricultural production facilities in recent years, the results of this study are expected to prevent damage to farms due to downtime caused by mistakes, faults, and aging.

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Section: Model Designmentioning

confidence: 99%

Artificial Intelligence-Based Fault Diagnosis and Prediction for Smart Farm Information and Communication Technology Equipment

Choe,

Lee

2023

Agriculture

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“…Lembo et al [5] study a fully graphical language Graphol, which is inspired by standard formalisms for conceptual modeling, similar to the UML class diagram and the ER model, but equipped with formal semantics. The authors also present several usability studies indicating that Graphol is suitable for quick adoption by conceptual modelers.…”

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confidence: 99%

Modern Trends in Multi-Agent Systems

Kenyeres,

Budinská,

Hluchý

et al. 2024

Future Internet

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“…5. The OWL-2 DL ontology defines temporal concepts and is used to describe the temporal characteristics of resources [34]. It also provides the lexicon for describing information related to duration, the relationships between the temporal locationsincluding date and time-of each moment, the intervals between moments, and their temporal order.…”

mentioning

confidence: 99%

Design and Implementation of a Malfunction Detection System for Livestock Ventilation Devices in Smart Poultry Farms

Kim

Lee

2022

Agriculture

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Smart livestock farming aims to improve the productivity of livestock through the provision of optimal housing, and it is developed using various sensors and actuators. Ventilation systems play a crucial role in smart livestock farming, including disease prevention and the processing of pollutants (ammonia and hydrogen sulfide) that are severely detrimental to livestock growth. Malfunctions in animal housing ventilation systems lead to mass mortality events. To address such issues, this study reports the design and implementation for a smart detection system for malfunctions in the ventilation devices installed in animal housing. This system is based on recurrent neural networks (RNNs) and implements the ontology method, considering sensor and controller data as the standard. A semantic sensor network ontology founded on a knowledge base was used to detect malfunctions, and stimulus-sensor-observation patterns were used to determine a sensor network within the smart barn. System activation and RNN model tests were used to test the malfunction detection system, and the error between actual data and predicted values was found to be 0.06889. These findings provide insight into the development of autonomous detection systems for device malfunctions and are essential for the development of smart livestock farming technologies.

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Graphol: A Graphical Language for Ontology Modeling Equivalent to OWL 2

Cited by 6 publications

References 49 publications

Artificial Intelligence-Based Fault Diagnosis and Prediction for Smart Farm Information and Communication Technology Equipment

Artificial Intelligence-Based Fault Diagnosis and Prediction for Smart Farm Information and Communication Technology Equipment

Modern Trends in Multi-Agent Systems

Design and Implementation of a Malfunction Detection System for Livestock Ventilation Devices in Smart Poultry Farms

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