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Semi-structured documents formatted with the Extensible Markup Language (XML) are gaining wide use by a whole range of applications including E-Commerce, E-Business, E-Science, Digital Libraries (DL), File Sharing, and in the last years especially by applications for Peer-to-Peer (P2P) systems. P2P architectures have been identified as an efficient means of ad-hoc collaboration and information sharing among large, diverse, and dynamic sets of user. However, current P2P search engines for XML-documents lack the use of information retrieval methods to efficiently search XML collections for relevant information. This paper proposes a search engine for P2P systems that applies an extension of the vector space model and exploits structural information to compute relevance of XML-documents, and thus may significantly improve retrieval performance.
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