# OAK: Ontology-Based Knowledge Map Model for Digital Agriculture

arXiv: https://arxiv.org/abs/2011.11442 · HTML: https://mlsanity.com/paper/2011.11442/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [Knowledge Representation in Digital Agriculture: A Step Towards Standardised Model](https://mlsanity.com/paper/2207.07740/) (2022)
- [OAK4XAI: Model towards Out-Of-Box eXplainable Artificial Intelligence for Digital Agriculture](https://mlsanity.com/paper/2209.15104/) (2022)
- [From Field Data to Global Food Systems Intelligence: A Semantic Graph Framework for Sustainable Wheat Production](https://mlsanity.com/paper/2502.19507/) (2025)

## Abstract (arXiv)

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.