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Extended Explicit Semantic Analysis for Calculating Semantic Relatedness of Web Resources

Author:Philipp Scholl, Doreen Böhnstedt, Renato Domínguez García, Christoph Rensing, Ralf Steinmetz
Date:September 2010
Kind:In proceedings - use for conference & workshop papers
Publisher:Springer Verlag
Book title:Sustaining TEL: From Innovation to Learning and Practice Proceedings of EC-TEL 2010
Editor:Martin Wolpers, Paul A. Kirschner, Maren Scheffel, Stefanie Lindstädt, Vania Dimitrova
Volume:Lecture Notes in Computer Science 6383
Keywords:Explicit Semantic Analysis, Semantic Relatedness, Wikipedia, Reference Corpus, Recommendation
Number of characters:32562
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.
German Abstract:Das Auffinden semantisch ähnlicher Dokumente ist eine grundlegende Aufgabe von Empfehlungssystemen. Explicit Semantic Analysis (ESA) ist ein Ansatz, der basierend auf Dokumentähnlichkeiten mit einem Referenzkorpus die semantische Verwandtschaft zwischen Termen oder Dokumenten misst. Üblicherweise wird als Referenzkorpus Wikipedia eingesetzt. Wir schlagen einige ESA-Erweiterungen (im Folgenden Extended Explicit Semantic Analysis genannt) vor, die weitere semantische Eigenschaften von Wikipedia einsetzen, wie z.B. die Artikel-Links und Kategorien. Wir zeigen, wie wir diesen Ansatz einsetzen, um Web-Ressourcen-Fragmente in einem Szenario des selbst-gesteuerten Ressourcen-basierten Lernens empfehlen zu können.
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