期刊
ARTIFICIAL INTELLIGENCE XXXIX, AI 2022
卷 13652, 期 -, 页码 238-251出版社
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-031-21441-7_17
关键词
Explainable AI; Knowledge map; Agriculture computing ontology; Knowledge management; Digital agriculture
类别
资金
- SFI Strategic Partnerships Programme [16/SPP/3296]
- Origin Enterprises Plc
Recent advances in machine learning have led to effective applications in Artificial Intelligence (AI), but there is a lack of human-understandable explanations for the results and decisions. Explainable Artificial Intelligence (XAI) aims to provide human-understandable explanations for decision-making and trained AI models. This paper introduces a framework, OAK4XAI, which utilizes a knowledge map model and ontology design to address this issue. The framework considers both data analysis and the semantic aspect of domain knowledge, providing consistent information and definitions.
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.
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