A Framework for Ontology Learning from Taxonomic Data

Abstract

Taxonomy is implemented in myriad areas of biological research and though structured it deals with the problem of information retrieval. Ontology is a very powerful tool for knowledge representation and literature also cites the conversion of taxonomies into ontologies. The automated ontology learning is developed to ward off the knowledge acquisition bottleneck; but thereof the limitation includes text understanding, knowledge extraction, structured labelling and filtering. The system, ASIUM, TEXT TO ONTO, DODDLE II, SYNDIKATE, HASTI, etc., includes some inadequacies and does not exclusively deal with taxonomic texts. The proposed system will deal with the taxonomic text available in agricultural system and will also enhance the algorithms thereby available. We also propose a framework for learning of the taxonomic text which will overcome the loopholes of ontology developed from generalized texts. Finally, a framework of comparison of the manually developed ontology and automatically developed ontology will be ensured.