Modeling Competency Questions-Based Ontology for the Domain of Maize Crop: SIMcOnto

Abstract

In this present time, there is rapid increase of various forms and structures of information across different domains of real world; for instance, agriculture. Because of this development, information is readily available; however, to retrieve the relevant information becomes a research issue to contend with. This identified research issue is, on the one hand, attributed to the unstructured representation of data, and on the other hand, attributed to the problem of word mismatch. Consequently, and in lieu of this, to retrieve relevant soils and irrigations data for maize crop in a more efficient structure becomes a challenge. Therefore, this research work aims to model soils and irrigations data for maize crop ontologically, which is christened as SIMcOnto. In order to achieve this objective, ontology which is a data modeling technique for complex knowledge representation is exploited. At the end, rule-based ontology is developed using the combined methodologies approach and written using Web Ontology Language (OWL2) in the syntax of RDF/XML. The rules leverage on the validated competency questions (CQs) which are modeled in first-order logic (FOL). During the course of the ontology development, the terminologies and the semantic rules are validated and verified by the domain experts and evaluation techniques. Therefore, the proposed SIMcOnto provides a machine represented knowledge-based modeling for soils and irrigations knowledge of maize crop. It is promising in retrieving a more precise and efficient information.