Extended Explicit Semantic Analysis for Calculating Semantic Relatedness of Web Resources

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 corpu...

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Bibliographic Details
Published inSustaining TEL: From Innovation to Learning and Practice pp. 324 - 339
Main Authors Scholl, Philipp, Böhnstedt, Doreen, Domínguez García, Renato, Rensing, Christoph, Steinmetz, Ralf
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2010
SeriesLecture Notes in Computer Science
Subjects
Explicit Semantic Analysis
Recommendation
Reference Corpus
Semantic Relatedness
Wikipedia
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Summary: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.
ISBN:3642160190
9783642160196
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-642-16020-2_22