Recommending Metadata Contents for Learning Objects Through Linked Data.

Attach metadata to digital objects effectively underlies the development of high-quality services in systems. This work explores how the metadata of a learning object represented as linked data, in a brand new repository, can be a facilitator to a more complete catalog and search with contents recommendations. The proposed approach underlies in DBpedia Spotlight for unstructured text annotation to deliver recommendations at the learning object cataloging phase and GEMET, a marine domain thesaurus, to expand marine searching terms. Each learning object is described with OBAA metadata as a set of triples stored in Resource Description Framework format to deliver interoperability and Linked Data compatibility.