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.