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Back Propagation, Domain Ontology, Knowledge Extraction, Agricultural Environment

In recent research, Ontology construction plays a major role for transforming raw texts into useful knowledge. The proposed method supports efficient retrieval with the help of ontology and applies combined techniques to train the data before taking into testing process. The proposed approach used the phrase-pairs to extract useful knowledge and utilized data mining techniques and neural network approach to express the knowledge well and also it improves the search speed and accuracy of information retrieval. This method avoids noise generation by analyzing the relevancy of tags to the retrieval process and shows somewhat better recall value compared to other methods. In this approach an optimized reasoner applied to reduce complexity in the key inference problem. The formulated ontology can help clearly expressing its meaning for various concepts and relations. Due to the increasing size of ontology repository, the matching process may take more time. To avoid this, this method forms a hierarchical structure with semantic interpretation of data. The system designed to eliminate domain-dependency with the help of dynamic labeling scheme using ontology as a base. In this paper, our proposed models were presented with ontology description using Ontology Web Language (OWL).