OAK: Ontology-Based Knowledge Map Model for Digital Agriculture
This work provides a method for organizing and exploiting agricultural knowledge, which is a problem for stakeholders and data mining processes in digital agriculture.
The paper addresses the challenge of organizing the vast amount of knowledge in digital agriculture by proposing an ontology-based knowledge map model. This model collects, stores, and exploits knowledge from various sources, serving either direct stakeholder use or as input for knowledge discovery processes.
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.