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Topic Extraction and Interactive Knowledge Graphs for Learning Resources

Author

Listed:
  • Ahmed Badawy

    (Telematic Engineering Department, University Carlos III de Madrid, 28911 Legane, Spain
    Computer Science Department, Faculty of Science, Minia University, EL-Minia 61519, Egypt)

  • Jesus A. Fisteus

    (Telematic Engineering Department, University Carlos III de Madrid, 28911 Legane, Spain)

  • Tarek M. Mahmoud

    (Computer Science Department, Faculty of Science, Minia University, EL-Minia 61519, Egypt
    Faculty of Computers and Artificial Intelligence, Sadat City University, Sadat City 32897, Egypt)

  • Tarek Abd El-Hafeez

    (Computer Science Department, Faculty of Science, Minia University, EL-Minia 61519, Egypt
    Computer Science Unit, Deraya University, EL-Minia 61765, Egypt)

Abstract

Humanity development through education is an important method of sustainable development. This guarantees community development at present time without any negative effects in the future and also provides prosperity for future generations. E-learning is a natural development of the educational tools in this era and current circumstances. Thanks to the rapid development of computer sciences and telecommunication technologies, this has evolved impressively. In spite of facilitating the educational process, this development has also provided a massive amount of learning resources, which makes the task of searching and extracting useful learning resources difficult. Therefore, new tools need to be advanced to facilitate this development. In this paper we present a new algorithm that has the ability to extract the main topics from textual learning resources, link related resources and generate interactive dynamic knowledge graphs. This algorithm accurately and efficiently accomplishes those tasks no matter how big or small the texts are. We used Wikipedia Miner, TextRank, and Gensim within our algorithm. Our algorithm’s accuracy was evaluated against Gensim, largely improving its accuracy. This could be a step towards strengthening self-learning and supporting the sustainable development of communities, and more broadly of humanity, across different generations.

Suggested Citation

  • Ahmed Badawy & Jesus A. Fisteus & Tarek M. Mahmoud & Tarek Abd El-Hafeez, 2021. "Topic Extraction and Interactive Knowledge Graphs for Learning Resources," Sustainability, MDPI, vol. 14(1), pages 1-21, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2021:i:1:p:226-:d:711542
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    References listed on IDEAS

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