Ontology Construction for Agri-Text Using Hybrid NLP with Deep Learning Methods

Developing an agricultural ontology will facilitate the advancement of agriculture in transferring information between fields and natural language processing (NLP). Grammatical and contextual comprehension of the domain data is required to construct a domain-specific ontology. Although there are datasets available for agriculture, there is a lack of standardized and large-scale annotated datasets developed specifically for the purpose of ontology development and relationship extraction. Thus, because of the unavailability of a structured and annotated domain-specific dataset, a standard methodology with a combination of both grammatical and contextual analysis is required for effective data processing. Though there are many approaches to lay the foundations for the agriculture domain-specific ontologies, in this paper, pretrained DeBerta with regular expressions and the Graph Attention Network (GAT) method with regular expressions for term with domain relations extraction are proposed. From the acquired entities and connections between the entities, an ontology graph is constructed. The proposed work is evaluated using performance measures and compared with existing work. It was found that the proposed Ontology Construction for Agriculture Domain (OCAD) method performs better than other methods. The proposed OCAD framework achieves a precision of 99.64%, a recall of 99.26%, and an F1 score of 99.5%, demonstrating strong performance within a domain-specific setting over existing methods.