Development of Background Ontology for Weather Systems through Ontology Learning | Zendy

Kaladevi Ramar | Zendy; A. Revathi | Zendy; Sivakumar Sridhar | Zendy

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Development of Background Ontology for Weather Systems through Ontology Learning

Author(s) -

Kaladevi Ramar,

A. Revathi,

Sivakumar Sridhar

Publication year - 2020

Publication title -

international journal of recent technology and engineering (ijrte)

Language(s) - English

Resource type - Journals

ISSN - 2277-3878

DOI - 10.35940/ijrte.d4334.018520

Subject(s) - wordnet , ontology , computer science , vocabulary , ontology based data integration , suggested upper merged ontology , upper ontology , information retrieval , domain (mathematical analysis) , natural language processing , ontology learning , word (group theory) , artificial intelligence , semantic web , linguistics , mathematical analysis , philosophy , mathematics , epistemology

Background or reference ontology is a common vocabulary for a system to share knowledge and support information integration. Weather system has more domain specific words, which are not fully covered by generic knowledge source like web and WordNet. For example, Temp is a word related with temperature in weather system, this kind meaning is not available in WordNet. Secondly, many new technical and scientific words are used and existing words also carry different senses. Thesauri usually cannot capture these new senses and words in time. Available background knowledge is insufficient to overcome the existing challenges and issues. This paper focuses on developing background ontology for weather system by enhancing existing knowledge bases. Finally the comparison is made between manually developed ontology and semi automatically developed ontology.

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