• BHENSDADIA PURVI HASMUKHBHAI
• PRIYANKA RAVAL

Machine learning is a technology design to build intelligent systems. These systems also have the ability to learn from past experience and analyse historical data. It provides results according to its experience. Semantic Web will improve more relationship, trust and question. Semantics opens up the web of data to Artificial Intelligence to do thinking for us. Semantics uses ontology that enlists all the properties and inter-relationship of the entities in a particular domain. These ontologies are required to be matched with each other to provide semantic interoperability and then how semantic reasoners can be used for knowledge representation and reasoning. Matching ontologies based on string similarity solves the interoperability issues and thus helps ontology to communicate with each other. The main objective of this research work is to predict correct ontology matching using classification algorithm such as Naïve Bayes. To improve performance by giving multiple evidences by classifier.

Naïve bayes classifier, ontology matching, ontology, string similarity

"Automatic Ontology matching using Heterogeneous evidences", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.423-427, April-2019, Available :http://www.jetir.org/papers/JETIR1904677.pdf

"Automatic Ontology matching using Heterogeneous evidences", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp423-427, April-2019, Available at : http://www.jetir.org/papers/JETIR1904677.pdf