  A constantly growing amount of information is available through the web.
Unfortunately, extracting useful content from this massive amount of data still
remains an open issue. The lack of standard data models and structures forces
developers to create adhoc solutions from the scratch. The figure of the expert
is still needed in many situations where developers do not have the correct
background knowledge. This forces developers to spend time acquiring the needed
background from the expert. In other directions, there are promising solutions
employing machine learning techniques. However, increasing accuracy requires an
increase in system complexity that cannot be endured in many projects. In this
work, we approach the web knowledge extraction problem using an expertcentric
methodology. This methodology defines a set of configurable, extendible and
independent components that permit the reutilisation of large pieces of code
among projects. Our methodology differs from similar solutions in its
expert-driven design. This design, makes it possible for subject-matter expert
to drive the knowledge extraction for a given set of documents. Additionally,
we propose the utilization of machine assisted solutions that guide the expert
during this process. To demonstrate the capabilities of our methodology, we
present a real use case scenario in which public procurement data is extracted
from the web-based repositories of several public institutions across Europe.
We provide insightful details about the challenges we had to deal with in this
use case and additional discussions about how to apply our methodology.
