论文标题
橡树：基于本体的数字农业知识图模型
OAK: Ontology-Based Knowledge Map Model for Digital Agriculture
论文作者
论文摘要
如今，数字农业已经积累了大量知识。这些知识和知识信息是从各种来源收集的，因此问题是如何组织这些知识，以便可以有效利用它。尽管可以使用本体论，基于规则的专家系统或根据数据挖掘过程构建的知识模型来代表有关农业实践的这种知识，但可扩展性仍然是一个空旷的问题。在这项研究中，我们提出了一个称为基于本体的知识图的知识表示模型，该模型可以从不同来源收集知识，存储IT并直接由利益相关者或作为知识发现过程（数据挖掘）的输入。提出的模型由两个阶段组成，1）构建一个本体，作为特定领域和数据挖掘概念的知识库，以及2）建立基于本体的知识图模型，用于代表和存储农作物数据集中挖掘的知识。该模型的框架已在农业领域实施。这是一个有效且可扩展的模型，可以用作知识存储库数字农业。
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.
