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

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... 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.