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.
Index Terms—Web, investigative learning, recommendation, linked open data, self-directed learning.
Kang Ting and Shinobu Hasegawa are with Japan Advanced Institute of Science and Technology, Japan (e-mail: s1910155@jaist.ac.jp, hasegawa@jaist.ac.jp).
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Cite:Kang Ting and Shinobu Hasegawa, "Semantic-Awareness Recommendation with Linked Open Data in Web-Based Investigative Learning," International Journal of Computer Theory and Engineering vol. 14, no. 2, pp. 62-72, 2022.
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