There are several educational resources distributed in different repositories that address to a wide range of subjects and different educational goals. The proper choice of these educational resources is a challenge. Recommendation systems may help users in this task. In order to generate personalized recommendations, it is important to identify information that will help to define user profile and assist in identifying his/her interests. The constant and ever-increasing use of social networks allows the identification of different information about profile, interests, preferences, style and behavior from the spontaneous interaction. This paper presents an infrastructure able to extract users’ profile and educational context, from the Facebook social network and recommend educational resources. The proposal is supported by Information Extraction Techniques and Semantic Web technologies for extraction, enrichment and definition of user’s profile and interests. The recommendation approach is based on learning objects repositories, linked data and video repositories. It takes advantage of user’s spent time at the web. The proposal evaluation was made from the development of a prototype, three proofs of concept and a case study. The evaluation showed users’ acceptance of extracted information about their educational interests, automatically generated from social network and enriched to find implicit interests. It was also validated the possibility of people recommendation, enabling the establishment of interest network, based on a specific subject, showing good partners to study and research.

The main objective of domain engineering is to provide domain information that helps the specification of domain applications. Some applications need to reuse information from multiple domains. Currently, there are several Domain Engineering methods that provide domain information using different representations that are stored in various formats. Due to the costs involved in a domain engineering initiative, it is important to be able to access all available domain information. This paper describes a retrieval agent system that provides access to information from multiple domains, regardless of its heterogeneity or distribution. Domain ontologies and an evolutionary model of the user's interests are some of the basic concepts used by the system to help users identify and retrieve relevant domain information.