General Information
    • ISSN: 1793-8201 (Print), 2972-4511 (Online)
    • Abbreviated Title: Int. J. Comput. Theory Eng.
    • Frequency: Quarterly
    • DOI: 10.7763/IJCTE
    • Editor-in-Chief: Prof. Mehmet Sahinoglu
    • Associate Editor-in-Chief: Assoc. Prof. Alberto Arteta, Assoc. Prof. Engin Maşazade
    • Managing Editor: Ms. Mia Hu
    • Abstracting/Indexing: Scopus (Since 2022), INSPEC (IET), CNKI,  Google Scholar, EBSCO, etc.
    • Average Days from Submission to Acceptance: 192 days
    • APC: 800 USD
    • E-mail: ijcte@iacsitp.com
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Editor-in-chief
Prof. Mehmet Sahinoglu
Computer Science Department, Troy University, USA
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.

IJCTE 2022 Vol.14(2): 62-72 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2022.V14.1311

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

Kang Ting and Shinobu Hasegawa

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.

Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).


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