The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Knowledge-based metadata extraction from PostScript files
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Automatic metadata generation & evaluation
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
The Sharable Content Object Reference Model (SCORM)—A Critical Review
ICCE '02 Proceedings of the International Conference on Computers in Education
Generation of description metadata for video files
Proceedings of the 14th International Conference on Computer Systems and Technologies
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The Shareable Content Object Reference Model (SCORM) is a widely adopted collection of specifications for web-based e-learning to which most Learning Management Systems adhere. While it allows reusability of content, it requires extensive, slow and expensive metadata annotation, and this fact prevents many content producers from properly creating and using Learning Objects. We propose an automatic metadata generation procedure that allows to label specific Learning Objects (scientific papers) with general metadata compliant to the SCORM. As some metadata are intrinsically unrelated to structure while others are strictly connected to structure, two different techniques were developed: one based on vocabularies and the other based on structural features. Results show that, in the provided context and for the "general" metadata category, the accuracy of annotations is comparable to that of a human expert.