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Enabling Remote School Education using Knowledge Graphs and Deep Learning Techniques

Publication Type : Conference Paper

Publisher : Elsevier

Source : Procedia Computer Science

Url :

Campus : Amritapuri

School : School of Computing

Center : Algorithms and Computing Systems

Year : 2022

Abstract : An automated question-answering system allows students to learn as an integral part of digitized learning. This system responds to queries using text. We also include a knowledge graph, which significantly enhances the model's intrigue and improves learners’ understanding. The features of knowledge entity extraction, information point evaluation and analysis, knowledge graph construction from unstructured text, and knowledge entity integration are all explored. The question-answering paradigm we suggest in this study uses knowledge graphs and BERT (Bidirectional Encoder Representations from Transformers) to provide diverse learners with quick feedback on the subject. In order to facilitate non-native learners’ understanding, we also include an English to Hindi translation. As a result, access to and continued learning can be very beneficial for educators.

Cite this Research Publication : Nair, Lekshmi S., M. K. Shivani. "Enabling remote school education using knowledge graphs and deep learning techniques." Procedia Computer Science 215 (2022): 618-625.

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