A query search technique for semantic web based on the dynamic distribution of backjumping method

Abstract

In the resource description language of the semantic web, vagueness of Resource Description Framework (RDF) data is playing an important role. The effective querying of the RDF data is increasing importance in semantic web. In this research, the Dynamic Distribution of BackJumping (DDBJ) algorithm is proposed in the fuzzy graph due to the ability of the algorithm to maintain its autonomy in the data. The fuzzy graph is generated from the triplets in the RDF and the vertices, edges are extracted from pattern matching techniques. The vertices and edges are applied in the DDBJ to suggest the query in the semantic web. To analyze the effectiveness of the proposed DDBJ, the two real datasets are used. The proposed DDBJ method has the f-measure of 59 %, while the state-of-art method such as backtracking has achieved 56 %. The result shows that the DDBJ method has the higher performance than existing method in query processing.