Web metadata extraction and semantic indexing for learning objects extraction

John Atkinson, Andrea Gonzalez, Mauricio Munoz, Hernan Astudillo

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


Secondary-school teachers are in constant need of finding relevant digital resources to support specific didactic goals. Unfortunately, generic search engines do not allow them to identify learning objects among semi-structured candidate educational resources, much less retrieve them by teaching goals. This article describes a multi-strategy approach for semantically guided extraction, indexing and search of educational metadata; it combines machine learning, concept analysis, and corpus-based natural language processing techniques. The overall model was validated by comparing extracted metadata against standard search methods and heuristic-based techniques for Classification Accuracy and Metadata Quality (as evaluated by actual teachers), yielding promising results and showing that this semantically guided metadata extraction can effectively enhance access and use of educational digital material.

Original languageEnglish
Pages (from-to)649-664
Number of pages16
JournalApplied Intelligence
Issue number2
StatePublished - Sep 2014
Externally publishedYes


  • Learning objects
  • Machine learning
  • Metadata extraction
  • Semantic analysis
  • Text mining


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