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Article
Semantic Information Retrieval for Personalized E-learning
2008 20th IEEE International Conference on Tools with Artificial Intelligence (2008)
  • Leyla Zhuhadar, Western Kentucky University
  • Olfa Nasraoui
Abstract
We present an approach for personalized retrieval in
an e-learning platform, that takes advantage of semantic
Web standards to represent the learning content and the
user/learner profiles as ontologies, and that re-ranks search
results/lectures based on how the contained terms map to
these ontologies. One important aspect of our approach is
the combination of an authoritatively supplied taxonomy by
the colleges, with the data driven extraction (via clustering)
of a taxonomy from the documents themselves, thus making
it easier to adapt to different learning platforms, and
making it easier to evolve with the document/lecture collection.
Our experimental results show that the learner’s
context can be effectively used for improving the precision
and recall in e-learning content retrieval, particularly by
re-ranking the search results based on the learner’s past
activities.
Keywords
  • onologies,
  • clustering,
  • e-learning,
  • semantic web standards
Publication Date
2008
Publisher Statement
Leyla Zhuhadar, Olfa Nasraoui, "Semantic Information Retrieval for Personalized E-learning", ICTAI 2008: 20th IEEE International Conference on Tools with Artificial Intelligence, Dayton, Ohio, USA, 2008.
Citation Information
Leyla Zhuhadar and Olfa Nasraoui. "Semantic Information Retrieval for Personalized E-learning" 2008 20th IEEE International Conference on Tools with Artificial Intelligence (2008)
Available at: http://works.bepress.com/leyla-zhuhadar/14/