Computer Science – Information Retrieval
Scientific paper
2010-12-03
Computer Science
Information Retrieval
9 pages, Third International Workshop on Semantic Aspects in Data Mining (SADM'10) in conjunction with the 2010 IEEE Internati
Scientific paper
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.
Ahlgren Oskar
Korhonen Pekka
Malo Pekka
Siitari Pyry
Wallenius Jyrki
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