Time and Accuracy Analysis of Relational vs RDF Databases

Abstract

As the scale and impact of the Internet has been growing, search engines have assumed an important role in the Web's infrastructure. Huge amount of information is available on Web but accurate and relevant results cannot be fetched from the search engines because these search engines retrieve data syntactically. To fetch the data as per the user’s requirements, semantic search engines are required. They are expected to be the next generation of web as Semantic Web provides a well defined meaning to the available data. In semantic web, data is represented in the languages like RDF and OWL and linked to the commonly accepted ontologies. To provide contextual information to the user, RDF databases were introduced. Semantic processing for data retrieval is accomplished by these RDF databases as they store the meta information about the data in the form of subject, predicate and object. The annotations increase the efficiency of semantic search by associating the metadata to the resources. Although data can be stored and retrieved efficiently in these types of databases they require some methodologies to query the repository and the query languages like SPARQL, SeRQL are used. In this thesis, two database approaches RDF database and relational database have been compared using two evaluation parameters viz. accuracy of search results and time required to access the databases. It has been experimentally evaluated that search results are faster and more accurate as compared to Relational database.