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.

该研究提出了基于本体的知识映射模型，以表示和存储作物种植的数据挖掘结果，以建立、维护和丰富知识发现过程，从而实现更高效的决策过程。