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.
%0 Book Section
%1 citeulike:7934031
%A Scholl, Philipp
%A Böhnstedt, Doreen
%A Dom\'ınguez Garc\'ıa, Renato
%A Rensing, Christoph
%A Steinmetz, Ralf
%B Sustaining TEL: From Innovation to Learning and Practice
%C Berlin, Heidelberg
%D 2010
%E Wolpers, Martin
%E Kirschner, Paul
%E Scheffel, Maren
%E Lindstaedt, Stefanie
%E Dimitrova, Vania
%I Springer Berlin / Heidelberg
%K e-learning text-analysis
%P 324--339
%R 10.1007/978-3-642-16020-2_22
%T Extended Explicit Semantic Analysis for Calculating Semantic Relatedness of Web Resources
%U http://dx.doi.org/10.1007/978-3-642-16020-2_22
%V 6383
%X 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.
%& 22
%@ 978-3-642-16019-6
@incollection{citeulike:7934031,
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.}},
added-at = {2018-03-19T12:24:51.000+0100},
address = {Berlin, Heidelberg},
author = {Scholl, Philipp and B\"{o}hnstedt, Doreen and Dom\'{\i}nguez Garc\'{\i}a, Renato and Rensing, Christoph and Steinmetz, Ralf},
biburl = {https://www.bibsonomy.org/bibtex/2e5347b3b1115f91e7300733b1065aba0/aho},
booktitle = {Sustaining TEL: From Innovation to Learning and Practice},
chapter = 22,
citeulike-article-id = {7934031},
citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-642-16020-2_22},
citeulike-linkout-1 = {http://www.springerlink.com/content/h278k08u30u0010v},
doi = {10.1007/978-3-642-16020-2_22},
editor = {Wolpers, Martin and Kirschner, Paul and Scheffel, Maren and Lindstaedt, Stefanie and Dimitrova, Vania},
interhash = {b1178f0b1204b4be3b30e58e37d33a09},
intrahash = {e5347b3b1115f91e7300733b1065aba0},
isbn = {978-3-642-16019-6},
keywords = {e-learning text-analysis},
pages = {324--339},
posted-at = {2010-10-01 14:11:45},
priority = {2},
publisher = {Springer Berlin / Heidelberg},
series = {Lecture Notes in Computer Science},
timestamp = {2018-03-19T12:24:51.000+0100},
title = {{Extended Explicit Semantic Analysis for Calculating Semantic Relatedness of Web Resources}},
url = {http://dx.doi.org/10.1007/978-3-642-16020-2_22},
volume = 6383,
year = 2010
}