A Quantitative Assessment Framework for Modelling and Evaluation Using Representation Learning in Smart Agriculture Ontology

Khadija Meghraoui, Teeradaj Racharak, Kenza El Kadi, Kenza El Kadi, Saloua Bensiali, Imane Sebari, Imane Sebari

2024

Abstract

Understanding agricultural processes and their interactions can be improved with trustworthy and precise models. Such modelling boosts various related tasks, making it easier to take informed decisions in the realm of advanced agriculture. In our study, we present a novel agriculture ontology, primarily focusing on crop production. Our ontology captures fundamental domain knowledge concepts and their interconnections, particularly pertaining to key environmental factors. It encompasses static aspects like soil features, and dynamic ones such as climatic and thermal traits. In addition, we propose a quantitative framework for evaluating the quality of the ontology using the embeddings of all the concept names, role names, and individuals based on representation learning (i.e. OWL2Vec*, RDF2Vec, and Word2Vec) and dimensionality reduction for visualization (i.e. t-distributed Stochastic Neighbor Embedding). The findings validate the robustness of OWL2Vec* among other embedding algorithms in producing precise vector representations of ontology, and also demonstrate that our ontology has well-defined categorization aspects in conjunction of the embeddings.

Paper Citation

in Harvard Style

Meghraoui K., Racharak T., El Kadi K., Bensiali S. and Sebari I. (2024). A Quantitative Assessment Framework for Modelling and Evaluation Using Representation Learning in Smart Agriculture Ontology. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 1044-1051. DOI: 10.5220/0012432900003636

in Bibtex Style

@conference{icaart24,
author={Khadija Meghraoui and Teeradaj Racharak and Kenza El Kadi and Saloua Bensiali and Imane Sebari},
title={A Quantitative Assessment Framework for Modelling and Evaluation Using Representation Learning in Smart Agriculture Ontology},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={1044-1051},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012432900003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - A Quantitative Assessment Framework for Modelling and Evaluation Using Representation Learning in Smart Agriculture Ontology
SN - 978-989-758-680-4
AU - Meghraoui K.
AU - Racharak T.
AU - El Kadi K.
AU - Bensiali S.
AU - Sebari I.
PY - 2024
SP - 1044
EP - 1051
DO - 10.5220/0012432900003636
PB - SciTePress