Recent machine learning approaches have been effective in Artificial Intelligence (AI) applications. They produce robust results with a high level of accuracy. However, most of these techniques do not provide human-understandable explanations for supporting their results and decisions. They usually act as black boxes, and it is not easy to understand how decisions have been made. Explainable Artificial Intelligence (XAI), which has received much interest recently, tries to provide human-understandable explanations for decision-making and trained AI models. For instance, in digital agriculture, related domains often present peculiar or input features with no link to background knowledge. The application of the data mining process on agricultural data leads to results (knowledge), which are difficult to explain. In this paper, we propose a knowledge map model and an ontology design as an XAI framework (OAK4XAI) to deal with this issue. The framework does not only consider the data analysis part of the process, but it takes into account the semantics aspect of the domain knowledge via an ontology and a knowledge map model, provided as modules of the framework. Many ongoing XAI studies aim to provide accurate and verbalizable accounts for how given feature values contribute to model decisions. The proposed approach, however, focuses on providing consistent information and definitions of concepts, algorithms, and values involved in the data mining models. We built an Agriculture Computing Ontology (AgriComO) to explain the knowledge mined in agriculture. AgriComO has a well-designed structure and includes a wide range of concepts and transformations suitable for agriculture and computing domains.

翻译：最近的机器学习方法在人工智能(AI)应用中是有效的,在人工智能(AI)应用方面是有效的,它们往往产生稳健的结果,并且具有高度的准确性;然而,这些技术大多不为支持其结果和决定提供人类难以理解的解释;它们通常充当黑盒,而且不容易理解如何作出决定;最近受到极大关注的可解释的人工智能(XAI)试图为决策和经过培训的AI模型提供人所能够理解的解释;例如,在数字农业中,有关领域往往具有与背景知识无联系的特殊性或输入性特征;农业数据数据数据挖掘过程的运用过程导致难以解释的结果(知识);在本文中,我们提出一个知识映射模型模型模型和知识设计设计,作为XAI(O)框架框架的一个框架(OAAK4XAI),这个框架不仅考虑数据分析过程的一部分,而且还考虑通过一个在线和知识地图模型,作为框架的模块提供的域域的语义方面;许多正在进行的XAI研究旨在提供准确和口头的模型,目的是提供准确的和口头的模型,用以解释数据定义;但是,我们提出的农业数据结构定义中,为既定的数值提供了一种解释。