Abstract This paper describes how the linked data growth on Internet and development of linked data application is awkward. This paper describes how SPARQL query work on open linked data cloud using semantic web. We have built the system that will collect the standard information about the music. The information is collected from different data source available in linked open data cloud (Freebase, DBPedia,... This paper describes how the linked data growth on Internet and development of linked data application is awkward. This paper describes how SPARQL query work on open linked data cloud using semantic web. We have built the system that will collect the standard information about the music. The information is collected from different data source available in linked open data cloud (Freebase, DBPedia, MusicBrainz, WikiData and BBC music) and linked all the data sets through linked data. Our results will give suggestion about the data source which has more particular information. It will work like the routing table. Our system results will tell about what information user can get better and more from which data source. It will give all the links in it about where to find information about music work. Read More Read Less

Abstract In this paper we addressed the Knowledge Description and Discovery in analytical way. We proposed algorithms where Queries are indexed as knowledge. We supported the possible implementation of the proposed structure.... In this paper we addressed the Knowledge Description and Discovery in analytical way. We proposed algorithms where Queries are indexed as knowledge. We supported the possible implementation of the proposed structure. Read More Read Less

Abstract The increasing numbers of textual documents from diverse sources such as different websites (e.g. social networks, news, magazines, blogs and medical recommendation websites), publications and articles and medical prescriptions leads to massive amounts of daily complex data. This phenomenon has caused many researchers to focus on analysing the content and measuring the similarities among the documents and texts to cluster them. One popular... The increasing numbers of textual documents from diverse sources such as different websites (e.g. social networks, news, magazines, blogs and medical recommendation websites), publications and articles and medical prescriptions leads to massive amounts of daily complex data. This phenomenon has caused many researchers to focus on analysing the content and measuring the similarities among the documents and texts to cluster them. One popular method to measure the similarity between documents is to represent the documents as vectors and measure the similarity among them based on the angle or Euclidean distance between each pair. By only considering these two criteria for similarity measurement, we may miss important underlying similarities in this area. We propose a new method, TS-SS, to measure the similarity level among documents, in such a way that one hopes to better understand which documents are more (or less) similar. This similarity level can be used as a handy measure for clustering and recommendation systems for documents. It also can be used to show top n similar documents to a particular document or a search query. Our study gives insights on the drawbacks of geometrical and non-geometrical similarity measures and provides a novel method to combine the other geometric criteria into a method to measure the similarity level among documents from new prospective. We apply Euclidean distance, Cosine similarity and our new method on four labelled datasets. Finally we report how these three geometrical similarity measures perform in terms of similarity level and clustering purity using four evaluation techniques. The evaluations’ results show that our new model outperforms the other measures. Read More Read Less