Combining fuzzy information from multiple systems (extended abstract)
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Search engines are among the most important services on the Web. Due to the scale of the ever-growing Web, classic centralized models and algorithms can no longer meet the requirements of a search system for the whole Web. Decentralization seems to be an attractive alternative. Consequently Web retrieval has received growing attention in the area of peer-to-peer systems. Decentralization of Web retrieval methods, in particular of text-based retrieval and link-based ranking as used in standard Web search engines have become subject of intensive research. This allows both to distribute the computational effort for more scalable solutions and to share different interpretations of the Web content to support personalized and context-dependent search. In this paper we first review existing studies about the algorithmic feasibility of realizing peer-to-peer Web search using text and link-based retrieval methods. From our perspective realizing peer-to-peer Web retrieval also requires a common framework that enables interoperability of peers using different peer-to-peer search methods. Therefore in the second part we introduce a common framework consisting of an architecture for peer-to-peer information retrieval and a logical framework for distributed ranking computation.