Authors:
Khadija Meghraoui
1
;
Teeradaj Racharak
2
;
Kenza El Kadi
1
;
3
;
Saloua Bensiali
4
and
Imane Sebari
1
;
3
Affiliations:
1
Unit of Geospatial Technologies for a Smart Decision, Hassan II Institute of Agronomy and Veterinary Medicine, Rabat, Morocco
;
2
School of Information Science, Japan Advanced Institute of Science and Technology, Ishikawa, Japan
;
3
School of Geomatics and Surveying Engineering, Hassan II Institute of Agronomy and Veterinary Medicine, Rabat, Morocco
;
4
Department of Applied Statistics and Computer Science, Hassan II Institute of Agronomy and Veterinary Medicine, Rabat, Morocco
Keyword(s):
Ontology, Ontology Embedding, Agriculture, Word Embedding, Ontology Evaluation.
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 algorithm
s in producing precise vector representations of ontology, and also demonstrate that our ontology has well-defined categorization aspects in conjunction of the embeddings.
(More)