  Nowadays, the huge amount of information distributed through the Web
motivates studying techniques to be adopted in order to extract relevant data
in an efficient and reliable way. Both academia and enterprises developed
several approaches of Web data extraction, for example using techniques of
artificial intelligence or machine learning. Some commonly adopted procedures,
namely wrappers, ensure a high degree of precision of information extracted
from Web pages, and, at the same time, have to prove robustness in order not to
compromise quality and reliability of data themselves. In this paper we focus
on some experimental aspects related to the robustness of the data extraction
process and the possibility of automatically adapting wrappers. We discuss the
implementation of algorithms for finding similarities between two different
version of a Web page, in order to handle modifications, avoiding the failure
of data extraction tasks and ensuring reliability of information extracted. Our
purpose is to evaluate performances, advantages and draw-backs of our novel
system of automatic wrapper adaptation.
