Innovative agricultural ontology construction using NLP methodologies and graph neural network

Abstract

Advancements in technology brought various innovations to agricultural practices. As a part of the development, establishing an agricultural ontology would unleash the growth of cross-domain agriculture and Natural Language Processing (NLP). For constructing such domain-based ontology, semantic and syntactic understanding of the domain data is needed. In agriculture, the availability of pre-determined domain-based data is not sufficient hence, a standard methodology with syntactic and general semantic features are required for processing the data. In this research work, Agricultural Domain based Ontology Construction (ADOC) is proposed and the overall framework has three approaches for establishing the agriculture domain based ontologies. The input text documents undergo anaphora resolution phase utilizing the semantic-based method. In the first method of ADOC the ontology is developed using the terms and relationships that are extracted from the NLP techniques. The second method of ADOC uses pretrained BERT model and Hearst patterns while the third model of ADOC is based on pretrained BERT with regular expressions and unsupervised Graph Neural Network (GNN) for creating the agricultural ontology. The efficacy of the proposed ADOC utilizing BERT with regular expressions and GNN method shows an outstanding result when compared to other proposed and prevailing systems, with a precision and recall of 96.67% and 98.31%.

Keywords

• Term extraction
• Relation extraction
• Natural language processing
• Regular expressions
• Graph neural network