Semantic-Awareness Recommendation with Linked Open Data in Web-Based Investigative Learning

Abstract

Web-based investigative learning provides a platform for learners to create their own learning scenarios by organizing knowledge over the web in a self-directed way. This kind of knowledge management activity helps learners to achieve a proper cognitive load on the investigation. However, it is difficult for learners to discover related concepts among a vast number of unstructured web resources concurrently with a better knowledge construction process. Therefore, this research aims to propose a method to recommend semantic-related concepts with Linked Open Data for learners during the investigation of the web-based investigative learning process. We proposed a Semantic-awareness Recommendation System that extracts the semantic related concepts from DBpedia by sending the regulated SPARQL query. In this work, generating a regulated concept map based on the initial question for the recommendation, three significant elements would be considered: Semantic relations, Concept Importance Estimation and Filtering.