Trusting Semi-structured Web Data

Abstract

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.I investigate about different aspects inherent to this problem: data, metadata and reasoning techniques useful to make adequate trust estimates. Research Question 1. The first problem that I focus on is the usage of trusted semi-structured Web data to make trust evaluations of semi-structured data (not necessarily coming from Web sources). This gives a first insight into the possibility to use Web data for assessing the trustworthiness of data. Hence the first research question is:Can Web data help the trust evaluation of semi-structured data?