Developing an agriculture ontology for extracting relationships from texts using Natural Language Processing to enhance semantic understanding

This paper outlines a methodology for developing an agriculture ontology to extract relationships from texts using web-scraping techniques, Natural Language Processing (NLP) and Artificial Intelligence (AI). The objective of the presented approach is to offer a deeper understanding of the connections among different concepts in the agriculture industry and enhance the decision-making processes. The proposed methodology comprises utilizing web-scraping techniques to gather text data pertaining to agriculture from sources that provide agri-related information. Subsequently, the gathered data is subjected to pre-processing utilizing NLP techniques in order to eliminate any extraneous or insignificant information. Then, a range of Machine-Learning and Deep Learning techniques, specifically Linear SVM, Random Forest, Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) network, are employed to derive significant insights from the pre-processed data. The ontology is constructed using Protégé through the identification of concepts and relationships derived from the extracted features using a rule-based methodology. The suggested approach was evaluated using a dataset consisting of articles related to agriculture. The results showcased the effectiveness of the suggested approach in creating an agriculture ontology that accurately identifies connections between concepts. Paper introduces a novel approach to creating an agriculture ontology by employing web-scraping techniques, NLP, and AI to extract semantic relationships from textual data. The proposed approach has the potential to enhance decision-making processes in agriculture by providing insights into the interrelationships among various concepts. This methodology is highly valuable for researchers and professionals in the agriculture sector and it can also be utilized in other fields to derive semantic relationships from textual data.