Enabling Remote School Education using Knowledge Graphs and Deep Learning Techniques

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 knowl...

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Bibliographic Details
Published in:Procedia computer science 2022, Vol.215, p.618-625
Main Authors: Nair, Lekshmi S, Shivani, M K, Jo Cheriyan, Sr
Format: Article
Language:English
Subjects:
Bidirectional Encoder Representations from Transformers
Knowledge Graph
Long Short-Term Memory
Machine Translation
Natural Language Processing
Question-Answering
Remote Education
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Summary: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.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2022.12.064