Educational content available on the web is playing an important role in the teaching and learning process. Learners search for different types of learning objects such as videos, pictures, and blog articles and use them to understand concepts they are studying in books and articles. The current search platforms provided can be frustrating to use. Either they are not specified for educational purposes or they are provided as a service by a library or a repository for searching a limited dataset of educational content. This paper presents a novel system for automatic harvesting and connecting of medical educational objects based on biomedical ontologies. The challenge in this work is to transform disjoint heterogeneous web databases entries into one coherent linked dataset. First, harvesting APIs were developed for collecting content from various web sources such as YouTube, blogging platforms, and PubMed library. Then, the system maps its entries into one data model and annotates its content using biomedical ontologies to enable its linkage. The resulted dataset is organized in a proposed NoSQL RDF Triple Store which consists of 2720 entries of articles, videos, and blogs. We tested the system using different ontologies for enriching its content such as MeSH and SNOMED CT and compared the results obtained. Using SNOMED CT doubled the number of linkages built between the dataset entries. Experiments of querying the dataset is conducted and the results are promising compared with simple text-based search.
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