Semantic Content Filtering with Wikipedia and Ontologies

TL;DR

This paper presents a framework that combines Wikipedia's semantic relatedness data with domain ontologies to improve document filtering, demonstrating superior performance over traditional machine learning classifiers on standard datasets.

Contribution

It introduces a novel approach integrating Wikipedia and ontologies for semantic content filtering, reducing reliance on manually built domain knowledge sources.

Findings

Outperforms SVM and C4.5 classifiers in filtering tasks

Shows robust performance on Reuters RCV1 and TREC-11 datasets

Utilizes Wikipedia's semantic relatedness to enhance content classification

Abstract

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.