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Relevance Ranking Tuning for Similarity Queries on XML Data

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2590))

Abstract

Similarity query techniques integrating semantics and structure of XML data have been recently investigated. Mostly, query relaxations are not fully exploited to retrieve data that approximate query conditions on data organization. In this paper we present a method to widen the spectrum of relevant data, and we define a measure to tune the ranking of results, so that also information on relevance quality of data retrieved can be inferred from scores.

Suppose the user does not remember the firstname “Elton” and, since “John” is a very common name, she wants to specify that “John” must be a lastname.

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© 2003 Springer-Verlag Berlin Heidelberg

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Ciaccia, P., Penzo, W. (2003). Relevance Ranking Tuning for Similarity Queries on XML Data. In: Bressan, S., Lee, M.L., Chaudhri, A.B., Yu, J.X., Lacroix, Z. (eds) Efficiency and Effectiveness of XML Tools and Techniques and Data Integration over the Web. DIWeb EEXTT 2002 2002. Lecture Notes in Computer Science, vol 2590. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36556-7_2

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  • DOI: https://doi.org/10.1007/3-540-36556-7_2

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00736-4

  • Online ISBN: 978-3-540-36556-3

  • eBook Packages: Springer Book Archive

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