Clustered approach to Web Search Using SVM as Load Balancing Module

Abstract

The rapid growth of the Internet has made the Web a popular place for collecting information. Today, Internet user access billions of web pages online using search engines. Information in the Web comes from many sources, including websites of companies, organizations, communications etc. Effective representation of Web search results remains an open problem in the Information Retrieval community. To overcome this, the relevant Web pages are often located close to each other in the Web graph of hyperlinks. It presents a graphical approach for entity resolution & complements the traditional methodology with the analysis of the entity-relationship (ER) graph constructed for the dataset being analyzed. It can significantly improve the quality of entity resolution. Using Support Vector Machines (SVMs) as supervised learning methods distributes the workload over the network by assigning the capacity to handle the number of requests at a time. Hence provide the stable system with quality results.

Keywords: Information Retrieval; Web; ER; SVM; Cluster;