With the tremendous growth of information available to end users through the Web, search engines come to play ever a more critical role. Nevertheless, because of their general-purpose approach, it is always less uncommon that obtained result sets provide a burden of useless pages. The next-generation Web architecture, represented by the Semantic Web, provides the layered architecture possibly allowing overcoming this limitation. The ontology for multiple search engines is written such that in this search engine for single query the final result is got from multiple search engines. After getting the user query result we can use the clustering. In this clustering the user query results is formed in the a to z form. the Several search engines have been proposed, which allow increasing information retrieval accuracy by exploiting a key content of Semantic Web resources, that is, relations. We can use web cache optimization in search engine to get fast retrieval of user query results. In this work I have used web cache optimization based on eviction method for semantic web search engine. In this paper, analization of both advantages and disadvantages of some current Web cache replacement algorithms including lowest relative value algorithm, least weighted usage algorithm and least unified-value (LUV) algorithm is done. Based on our analysis, we proposed a new algorithm, called least grade replacement (LGR), which takes recency, frequency, perfect-history, and document size into account for Web cache optimization.

semantic web multiple search engines ontology Clustering web caching LRU LGR LUV algorithm