Web Service Discovery Using Bio-Inspired Holistic Matching Based Linked Data Clustering Model for RDF Data

Intorduction: Resource Description Framework (RDF) is the de-facto standard language model for semantic data representation on semantic web. Designing an efficient management of RDF data with huge volume and efficient querying techniques are the primary research areas in semantic web. Methods: So far, several RDF management methods have been offered with data storage designs and query processing algorithms for data retrieval. However, these methods do not adequately address the presence of irrelevant links that degrade the performance of web service discovery. In this paper, we propose a Bio-inspired Holistic Matching based Linked Data Clustering (BHM-LDC) technique for efficient management and querying of RDF data. This technique is essentially based on three algorithms which are designed for RDF data storing, clustering the linked data and web service discovery respectively. Initially, the BHM-LDC technique store the RDF dataset as graph based linked data. Results and Discussion: Then, an Integrated Holistic Entity Matching based Distributed Genetic Algorithm (IHEM-DGA) is proposed to cluster the linked data. Finally, a sub-graph matching based web Service discovery Algorithm that uses the clustered triples has been proposed to find the best web services. Our experimental results reveal the performance of the proposed web service discovery approach by applying on business RDF dataset.