Enabling Remote School Education using Knowledge Graphs and Deep Learning Techniques

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

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.