Extended explicit semantic analysis for calculating semantic relatedness of web resources

Authors:
Philipp Scholl;Doreen Böhnstedt;Renato Domínguez García;Christoph Rensing;Ralf Steinmetz
Affiliations:
Multimedia Communications Lab, KOM, Technische Universität Darmstadt, Darmstadt, Germany;Multimedia Communications Lab, KOM, Technische Universität Darmstadt, Darmstadt, Germany;Multimedia Communications Lab, KOM, Technische Universität Darmstadt, Darmstadt, Germany;Multimedia Communications Lab, KOM, Technische Universität Darmstadt, Darmstadt, Germany;Multimedia Communications Lab, KOM, Technische Universität Darmstadt, Darmstadt, Germany
Venue:
EC-TEL'10 Proceedings of the 5th European conference on Technology enhanced learning conference on Sustaining TEL: from innovation to learning and practice
Year:
2010

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Abstract

Finding semantically similar documents is a common task in Recommender Systems. Explicit Semantic Analysis (ESA) is an approach to calculate semantic relatedness between terms or documents based on similarities to documents of a reference corpus. Here, usually Wikipedia is applied as reference corpus. We propose enhancements to ESA (called Extended Explicit Semantic Analysis) that make use of further semantic properties of Wikipedia like article link structure and categorization, thus utilizing the additional semantic information that is included in Wikipedia. We show how we apply this approach to recommendation of web resource fragments in a resource-based learning scenario for self-directed, on-task learning with web resources.