National Academy of Agricultural Sciences (NAAS)
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PRINT ISSN : 2319-7692
Online ISSN : 2319-7706 Issues : 12 per year Publisher : Excellent Publishers Email : editorijcmas@gmail.com / submit@ijcmas.com Editor-in-chief: Dr.M.Prakash Index Copernicus ICV 2018: 95.39 NAAS RATING 2020: 5.38 |
Ontology is one of the most popular knowledge representation techniques. Ontology based knowledgebase system is very well structured. Building of knowledgebase manually is a quite cumbersome process. This knowledge acquisition bottle neck can be overcome by making the ontology building process automated. Accordingly, the ontology learning came into the scene. In ontology learning, the natural text is taken as an input and building of the ontology is done. One of its drawbacks is that taking input from natural text to ontology development several limitations are being encountered. In this research work, we have taken two aspects of ontology building. We have firstly inducted taxonomy and secondly extracted the property of the taxonomy automatically from the semi structured text. For demonstration purpose, we have taken USDA soil taxonomy; in which we run our algorithm in a single chapter ‘Alfisol’ and got a very significant result. This novel method of ontology building process based on connectives facilitate us the minimal use of corpus thus waiving of its tediousness.
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