AN OWL BASED ONTOLOGY MODEL FOR CLIMATIC CONDITIONS KNOWLEDGE ON MAIZE CROP FARMING: SCENARIO FOR ENHANCING OWL’ OBJECT PROPERTY FOR INTELLIGENT SYSTEMS

Abstract

The exponential growths of data in heterogeneous forms cut across all human endeavors and disciplines, agriculture for instance. Accessing knowledge in respect to climatic conditions that affects maize crop during planting stage is very significant in order to boost and maintain the crop’s maximum yields. However, retrieving or accessing the relevant knowledge to a user’s query intension becomes an issue. Therefore, a promising solution towards mitigating this challenge of retrieving relevant information as a result of natural language ambiguity is by modeling data ontologically. Ontology is a data modeling technique for knowledge representation in a machine understandable format. To this end, this paper aims to model an OWL-based ontology for climatic conditions knowledge affecting maize crop during planting stage and enhance the object properties of the concepts in terms of synonyms by using hybridization of Fox-Gruninger, Methontology and FAO-Based ontology development methodologies and written using OWL2 Web Ontology Language RDF/XML syntax. The correctness of the ontology’s content and correctness of the ontology development have been constantly validated by the domain experts and via experiments. Thus, the proposed JENA based system provided a relevant knowledge based on user’s queries of the subject matter in a more accurate and timely information.