The aim of this paper is to present an initiative of application of the Linked Data principles to promote data interoperability between heterogeneous OpenCourseWare (OCW) repositories and to enhance the search and discovery of contents created and shared by the universities. Design/methodology/approach This paper is a case study of how Linked Data technologies can be applied for the enhancement of open learning contents. Findings Results presented under the umbrella of OpenCourseWare Consortium (OCWC) and OCW-Universia consortium, as the integration and access to content from different repositories OCW and the development of a query method to access these data, reveal that Linked Data would offer a solution to filter and select semantically those open educational contents, and automatically are linked to the Linked Open Data Cloud. Originality/value The new OCW integration with Linked Data adds new features to the initial framework including improved query mechanisms and interoperability. * OCW-Universia promotes and disseminates the OCW concept in Ibero-America. Consuming and producing linked open data: the case of OpenCourseWare 3

In recent years, the use of recommender systems has become popular on the web. To improve recommendation performance, usage, and scalability, the research has evolved by producing several generations of recommender systems. There is much literature about it, although most proposals focus on traditional methods’ theories and applications. Recently, knowledge graph-based recommendations have attracted attention in academia and the industry because they can alleviate information sparsity and performance problems. We found only two studies that analyze the recommendation system’s role over graphs, but they focus on specific recommendation methods. This survey attempts to cover a broader analysis from a set of selected papers. In summary, the contributions of this paper are as follows: (1) we explore traditional and more recent developments of filtering methods for a recommender system, (2) we identify and analyze proposals related to knowledge graph-based recommender systems, (3) we present the most relevant contributions using an application domain, and (4) we outline future directions of research in the domain of recommender systems. As the main survey result, we found that the use of knowledge graphs for recommendations is an efficient way to leverage and connect a user’s and an item’s knowledge, thus providing more precise results for users.

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The Linked Data initiative is considered as one of the most effective alternatives for creating global shared information spaces, it has become an interesting approach for discovering and enriching open educational resources data, as well as achieving semantic interoperability and re-use between multiple OER repositories. The notion of Linked Data refers to a set of best practices for publishing, sharing and interconnecting data in RDF format. Educational repositories managers are, in fact, realizing the potential of using Linked Data for describing, discovering, linking and publishing educational data on the Web. This work shows a data architecture based on semantic web technologies that support the inclusion of open educational materials in massive online courses. The authors focus on a type of openness: open of contents as regards alteration i.e. freedom to reuse the material, to combine it with other materials, to adapt, and to share it further under an open license.

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