Publication

Abstract : An automated question-answering system aims to deliver answers to the questions based on an input text. Such systems are based on text processing and require extended processing time. Knowledge graphs for question answering have proven to be an efficient approach. The knowledge graphs can be applied in teaching-learning to make more efficient remote education. Developing a knowledge graph from unstructured text, processing and evaluating knowledge points, extracting knowledge entities, and integrating them are all focused. This article proposes a Question answering model incorporating a Knowledge graph and the pre-trained BERT(Bidirectional Encoder Representation from Transformers) for learning purposes. This model helps in assisting learners of all ages by providing immediate feedback. Hence it can be highly beneficial to students to obtain access to and continue remote learning.