  The use of domain knowledge is generally found to improve query efficiency in
content filtering applications. In particular, tangible benefits have been
achieved when using knowledge-based approaches within more specialized fields,
such as medical free texts or legal documents. However, the problem is that
sources of domain knowledge are time-consuming to build and equally costly to
maintain. As a potential remedy, recent studies on Wikipedia suggest that this
large body of socially constructed knowledge can be effectively harnessed to
provide not only facts but also accurate information about semantic
concept-similarities. This paper describes a framework for document filtering,
where Wikipedia's concept-relatedness information is combined with a domain
ontology to produce semantic content classifiers. The approach is evaluated
using Reuters RCV1 corpus and TREC-11 filtering task definitions. In a
comparative study, the approach shows robust performance and appears to
outperform content classifiers based on Support Vector Machines (SVM) and C4.5
algorithm.
