Journal
COMPUTERS AND ELECTRONICS IN AGRICULTURE
Volume 199, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.compag.2022.107127
Keywords
Knowledge maps; Knowledge representation; Knowledge management system; Agriculture computing ontology; Digital agriculture; Data mining
Funding
- SFI Strategic Partnerships Programme [16/SPP/3296]
- Origin Enterprises Plc
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This research introduces a novel ontology-based knowledge map model for storing and managing data mining results in crop farming to facilitate the process of knowledge discovery. The proposed model is dynamic and allows easy access, updates, and exploitation of knowledge, while the system architecture includes knowledge modelling, extraction, assessment, publishing, and exploitation.
In recent years, data science has evolved significantly. Data analysis and mining processes become routines in all sectors of the economy where datasets are available. Vast data repositories have been collected, curated, stored, and used for extracting knowledge. And this is becoming commonplace. Subsequently, we extract a large amount of knowledge, either directly from the data or through experts in the given domain. The challenge now is how to exploit all this large amount of knowledge that is previously known for efficient decision-making processes. Until recently, much of the knowledge gained through a number of years of research is stored in static knowledge bases or ontologies, while more diverse and dynamic knowledge acquired from data mining studies is not centrally and consistently managed. In this research, we propose a novel model called ontology-based knowledge map to represent and store the results (knowledge) of data mining in crop farming to build, maintain, and enrich the process of knowledge discovery. The proposed model consists of six main sets: concepts, attributes, relations, transformations, instances, and states. This model is dynamic and facilitates the access, updates, and exploitation of the knowledge at any time. This paper also proposes an architecture for handling this knowledge-based model. The system architecture includes knowledge modelling, extraction, assessment, publishing, and exploitation. This system has been implemented and used in agriculture for crop management and monitoring. It is proven to be very effective and promising for its extension to other domains.
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