Semantic Analysis of Wikipedia documents using Ontology

Abstract

There is a boom in the growth of information available freely on the web where a search engine builds for a decisive component in understanding the content of the web pages and also serving the user queries according to their relevant information. The semantic web offers a hopeful approach in this context, ontologies can semantically seize concepts for any issue which will empower tools to accord the data semantically. In this paper, a proposed technique is developed which uses a score or weight based semantic relation between the user queries and gives a more relevant result. This system is moderated to Wikipedia related article as they are extracted from Wikipedia api. The similarity level between two articles is computed based on keyword content by computing similarity between two documents. We study various proposal in this regard thus the proposed system tries to optimize the results and the state-of-the-art analysis is presented. Likened to other similarity method, the proposed technique shows the highest Pearson correlation coefficient.