Trusting Semi-structured Web Data

  • Davide Ceolin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7882)


The growth of the Web brings an uncountable amount of useful information to everybody who can access it. These data are often crowdsourced or provided by heterogenous or unknown sources, therefore they might be maliciously manipulated or unreliable. Moreover, because of their amount it is often impossible to extensively check them, and this gives rise to massive and ever growing trust issues. The research presented in this paper aims at investigating the use of data sources and reasoning techniques to address trust issues about Web data. In particular, these investigations include the use of trusted Web sources, of uncertainty reasoning, of semantic similarity measures and of provenance information as possible bases for trust estimation. The intended result of this thesis is a series of analyses and tools that allow to better understand and address the problem of trusting semi-structured Web data.


Semantic Similarity Measure Provenance Information User Reputation Subjective Logic Uncertainty Reasoning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Davide Ceolin
    • 1
  1. 1.VU UniversityAmsterdamThe Netherlands

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