Adaptive Semantic Indexing Of Documents For Locating Relevant Information In P2P Networks

Locating relevant information in Peer-to-Peer (P2P) system is a challenging problem. Conventional approaches use flooding to locate the content. It is no longer applicable due to massive information available upfront in the P2P systems. Sometime, it may not be even possible to return small percent of relevant content for a search if it is an unpopular content. In this paper, we present adaptive semantic P2P content indexed system. Content indices are generated using topical semantics of documents derived using Wordnet ontology. Similarities between document hierarchies are computed using information theoretic approach. It enables locating and retrieval of contents with minimum document movement, search space and nodes to be searched. Results illustrate that our work can achieve results better than Content Addressable Network (CAN) semantic P2P Information Retrieval (IR) system. Contrary to CAN semantic P2P IR system, we have used content aware and node aware bootstrapping instead of random bootstrapping of search process.