Abstract

Abstract Web-3.0 provides an easy way to utilize the in-depth knowledge of the huge data that grows day-by-day in the internet Our aim with this paper is to work with the Linked Open Data Cloud data, where the main problem wi th the dataset is inconsistencies, bulkiness. We are exploring bibliographic data which is one of the cloud data. The authors found some useful information in the dataset that should be explored for judging the improvement of the search query's result. After analysis we came to know that many of the papers residing in RKBExplorer did not have keyword information. Because of that the search engine based on the RKBExplorer only able to use the information in this database going to retrieve the papers, authors of that paper and their related cited papers with given paper author or title. But assume the situation where the user wants to enter the search string, then what would be the result? Would it retrieve all the related paper even if their keywords are not assigned? In this paper we are trying to answer this question, with the help of data mining algorithm ARM on the features retrieved from the RDF data. We have developed a novel approach through which we can answer the user's query which is mixture of important the strings, we called them tags of the papers