@article{10.1007/s10489-014-0557-6,
    abstract = "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. © 2014 Springer Science+Business Media New York.",
    number = "2",
    year = "2014",
    title = "Web metadata extraction and semantic indexing for learning objects extraction",
    volume = "41",
    keywords = "Learning objects , Machine learning , Metadata extraction , Semantic analysis , Text mining",
    pages = "649-664",
    doi = "10.1007/s10489-014-0557-6",
    journal = "Applied Intelligence",
    author = "Gonzalez, Andrea and Atkinson, J. and Astudillo, Hernán and Munoz, Mauricio"
}

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