Web data knowledge extraction

Juan M. Tirado, Ovidiu Serban, Qiang Guo, Eiko Yoneki

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 reuse of large pieces of code among projects. Our methodology differs from similar solutions in its expert-driven design. This design makes it possible for the 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.

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