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.

中文翻译：

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OAK：基于本体的数字农业知识地图模型

如今，数字农业已积累了大量知识。这些知识和专有技术信息是从各种来源收集的，因此问题是如何组织这些知识，以便可以对其进行有效利用。尽管可以使用本体，基于规则的专家系统或通过数据挖掘过程构建的知识模型来表示有关农业实践的知识，但是可伸缩性仍然是一个未解决的问题。在这项研究中，我们提出了一种知识表示模型，称为基于本体的知识地图，该模型可以从不同来源收集知识，进行存储，并可以由涉众直接利用，也可以作为知识发现过程（数据挖掘）的输入。提议的模型包括两个阶段，1）建立一个本体作为特定领域和数据挖掘概念的知识库，并且2）建立基于本体的知识图模型，以表示和存储在作物数据集上挖掘的知识。该提议模型的框架已在农业领域实施。它是一种高效且可扩展的模型，可以用作数字农业的知识库。