Intelligent Systems with Applications (Nov 2022)
MaCOnto: A robust maize crop ontology based on soils, fertilizers and irrigation knowledge
Abstract
The demand for relevant information in a timely manner portrays the significance of knowledge management in all areas of lives; for instance, agriculture. To this end, soils, fertilizers and irrigation as agronomic concepts are essential knowledge inputs for any crops, such as maize. Conversely, there is always difficulty in timely retrieval of these relevant information owing to the unstructured nature of data in repositories, and complexity of concepts mismatch. Sequel to this development, ontology, a semantic data modeling technique is promising as it has been recently employed to deal with these challenges across different domains. However, the robustness of ontology, in terms of semantic expressivity of hidden knowledge, and autonomous growth of ontology leave some gaps to contend with. In view of this development, this research aims to design a robust OWL Rule based ontology for maize crop domain by considering primarily soils, fertilizers and irrigation agronomic concepts capable to evolve autonomously. The proposed ontology herein christened MaCOnto, is developed using the adapted six steps ontology-engineering principle. Over 1,430 entities are encoded in OWL; eighty Competency Questions (CQs) validated by domain experts are modeled in FOL, and implemented as rules via SWRL. Thus, the ontology is queried by SQWRL. Besides, the novel algorithmic design for the ontology to autonomously evolve is implemented in Java environment by employing WordNet. The results obtained from structural based evaluation show an outstanding performance across the eight metrics. Similarly, the results of the competency-based evaluation are also promising. Therefore, the proposed MaCOnto is a robust application based ontology capable to infer and responds to user's query based on its contextual information.
