Teaching learning based optimization in semantic web of distributed rdf

Abstract

Semantic web data use as a unified data model in various areas, such as Bioinformatics, media data, Wikipedia, social networks, and government open data. Sharing information among people using semantic web helps to understand and manipulation of information. In the semantic web, the Resource Description Framework (RDF) denotes the linked data. The logical data is represented as RDF model to manage the unformatted data and it provides an ability to machine interpretability of data. The major problem on the web is to handle the large volume of the data that also has other challenges like query processing and optimization over widely distributed RDF data. In this research, the Teacher Learning based Optimization (TLBO) algorithm is proposed for the query optimization to reduce query cost, and optimize the computation time of the query. The TLBO technique select the suitable location and size of the population based on the data that effectively provide the solution for the distributed data i.e.., triple pattern of semantic web. The experimental result showed that the TLBO in query optimization performed well in the manner of query computation time compared to existing methods like MARVEL. Additionally, the results showed that the proposed TLBO model achieved nearly 4.93 seconds for executing the multiple queries in LUBM dataset.