A knowledge graph is an essential and trending technology with great applications in entity recognition, search, or question answering. There are a plethora of methods in natural language processing for performing the task of Named entity recognition; however, there are very few methods that could provide triples for a domain-specific text. In this paper, an effort has been made towards developing a system that could convert the text from a given textbook into triples that can be used to visualize as a knowledge graph and use for further applications. The initial assessment and evaluation gave promising results with an F1 score of 82%.
@article{arxiv.2111.10692,
title = {Textbook to triples: Creating knowledge graph in the form of triples from AI TextBook},
author = {Aman Kumar and Swathi Dinakaran},
journal= {arXiv preprint arXiv:2111.10692},
year = {2021}
}