OAK: Ontology-Based Knowledge Map Model for Digital Agriculture

TL;DR

This paper introduces an ontology-based knowledge map model for digital agriculture that efficiently organizes and exploits diverse agricultural knowledge sources, enhancing scalability and usability for stakeholders and data mining.

Contribution

The paper presents a novel, scalable ontology-based knowledge map model that integrates knowledge from multiple sources and supports knowledge discovery in digital agriculture.

Findings

The model effectively consolidates agricultural knowledge from various sources.

It demonstrates improved scalability over existing knowledge representation methods.

The framework supports direct stakeholder use and data mining applications.

Abstract

Nowadays, a huge amount of knowledge has been amassed in digital agriculture. This knowledge and know-how information are collected from various sources, hence the question is how to organise this knowledge so that it can be efficiently exploited. Although this knowledge about agriculture practices can be represented using ontology, rule-based expert systems, or knowledge model built from data mining processes, the scalability still remains an open issue. In this study, we propose a knowledge representation model, called an ontology-based knowledge map, which can collect knowledge from different sources, store it, and exploit either directly by stakeholders or as an input to the knowledge discovery process (Data Mining). The proposed model consists of two stages, 1) build an ontology as a knowledge base for a specific domain and data mining concepts, and 2) build the ontology-based knowledge map model for representing and storing the knowledge mined on the crop datasets. A framework of the proposed model has been implemented in agriculture domain. It is an efficient and scalable model, and it can be used as knowledge repository a digital agriculture.