A Linked Dataset of medical educational resources

Reusable educational resources became increasingly important for enhancing learning and teaching experiences, particularly in the medical domain where resources are particularly expensive to produce. With respect to this, research has aimed at improving interoperability across educational resources metadata repositories, which led to a fragmented landscape of competing metadata schemas, such as IEEE LOM or OAI-DC, and interface mechanisms, such as OAI-PMH or SQI. Thus, the major issue of educational resource federation is the heterogeneity challenge of metadata and data. In this paper, we illustrate a medical educational dataset (mEducator Linked Educational Resources dataset) that is published as part of the Linked Open Data cloud following Linked Data (LD) principles. The dataset contains educational resource metadata harvested from ten different (medical) educational institutes. In addition, the dataset has been improved and enriched by deploying a range of LD-based techniques and datasets. We introduce a Semantic Web Service-based data extracting mechanism that is exploited for services and data integration in order to address heterogeneous metadata problems together with deployed enrichment and clustering mechanisms. The paper also discusses the data accessibility via a set of dedicated APIs, use cases and applications, which exploit our dataset together with some statistics and evaluation results.