Web data reconciliation: Models and experiences

Lorenzo Blanco, Valter Crescenzi, Paolo Merialdo, Paolo Papotti

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations


An increasing number of web sites offer structured information about recognizable concepts, relevant to many application domains, such as finance, sport, commercial products. However, web data is inherently imprecise and uncertain, and conflicting values can be provided by different web sources. Characterizing the uncertainty of web data represents an important issue and several models have been recently proposed in the literature. This chapter illustrates state-of-the-art Bayesan models to evaluate the quality of data extracted from the Web and reports the results of an extensive application of the models on real life web data. Experimental results show that for some applications even simple approaches can provide effective results, while sophisticated solutions are needed to obtain a more precise characterization of the uncertainty.

Original languageEnglish (US)
Title of host publicationSearch Computing
Subtitle of host publicationBroadening Web Search
EditorsStefano Ceri, Marco Brambilla
Number of pages15
StatePublished - Dec 1 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


Dive into the research topics of 'Web data reconciliation: Models and experiences'. Together they form a unique fingerprint.

Cite this