vioft2nntf2t|tblJournal|Abstract_paper|0xf4ff14c7200000007d4b050001000a00 Ontology is defined as shared specification of conceptual vocabulary used for formulating knowledge-level theories about a domain of discourse. Dataset is created by manually collecting information about different diseases related to crops. Ontology modeling is used for knowledge representation of various domains. India is an agricultural based economic country. Majority of Indian population relies on farming but the technologies are sparsely used for the aid of farmers. Ontology based modeling for agricultural knowledge can change this scenario. The farmers can understand it easily in their native language. We proposed a system which will model and extract knowledge in Marathi language. In this paper, we review various existing agriculture ontology’s along with some of Natural Language Processing (NLP) models. Model ontology for agriculture domain system aims to retrieve relevant answers to the farmer’s query. We explored Rule-Based and Conditional Random Fields based models for Ontology extraction. The extraction methods and preprocessing phases of proposed system is discussed.
Prachi Dalvi, Varsha Mandave, Madhu Gothkhindi, Ankita Patil, S. Kadam, Soudamini Pawar
D Y Patil College of Engineering, India
Ontology Modeling, Agriculture, NLP, Marathi, Domain Ontology
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 7 , Issue: 1 , Pages: 1359-1365 )
Date of Publication :
October 2016
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371
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