Correlating educational documents from different sources through graphs and taxonomies - Saraiva, Márcio de Carvalho; Medeiros, Claudia Bauzer (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2018: Anais do XXXIII Simpósio Brasileiro de Banco de DadosDO - 10.5753/sbbd.2018.22224, 2018)
Digital educational documents are growing in size and variety, and scientists are facing difficulties to find their way through them. One of the initiatives that have emerged to solve this problem involves the use of automatic classification algorithms. However, it is difficult to analyze implicit relationships among topics of materials. This paper presents CIMAL, a framework for enabling flexible access to material stored in arbitrary repositories. CIMAL combines semantic classification, taxonomies and graphs to elicit relationships among topics of educational documents. We validated our work using materials from Coursera (courses offered by Johns Hopkins University and University of Michigan) and a Higher Education Institute, from Brazil.