Abstract
Search engines (SE) were only considered in presenting the results of a search query ranked according to a certain algorithm. This means all users will see the same results for the same query (at the same time). Although these users differ in many aspects, this fact was not considered in modern search engines. Peer-to- Peer SEs have tried to imitate centralized ones, with little success. They were faced with massive amount of data, very dynamic structure of the environment and a big number of peers. The real power of a p2p network was not utilized sufficiently, which is the peers themselves. Peers are a representation of human identities in the internet. A peer (or a human) can be categorized by the following criteria: the language it speaks, the country it comes from, the age group it belongs to and the human character it falls under. This very peer in turn has many interests, and so it will visit web pages that match these interests. This information for this peer and million others will be then saved in the p2p network. With proper interpretation of this information a semantic web search engine can be built and a systematic method can be used to rank the result of a query according to the number of visited web pages visited by peers that have the same criteria as the query initiator.
In this paper we present LaBeeB, an innovative p2p web search engine that can resolve user queries effectively in a semantic fashion and can then rank the results based on human factors.
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Amour, M.A. (2012). LaBeeB: Systematic Peer Clustering for Building a Semantic Peer-to-Peer Web Search Engine. In: Unger, H., Kyamaky, K., Kacprzyk, J. (eds) Autonomous Systems: Developments and Trends. Studies in Computational Intelligence, vol 391. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24806-1_29
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DOI: https://doi.org/10.1007/978-3-642-24806-1_29
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