# Textbook to triples: Creating knowledge graph in the form of triples from AI TextBook

arXiv: https://arxiv.org/abs/2111.10692 · HTML: https://mlsanity.com/paper/2111.10692/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 20 Nov 2021.

## Builds on (most cited first, 4 of 4)

- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [Variational Reasoning for Question Answering with Knowledge Graph](https://mlsanity.com/paper/1709.04071/) (2017)
- [SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications](https://mlsanity.com/paper/1704.02853/) (2017)
- [ICDM 2019 Knowledge Graph Contest: Team UWA](https://mlsanity.com/paper/1909.01807/) (2019)

## Built on by (most cited first, 1 of 1)

- [FabKG: A Knowledge graph of Manufacturing Science domain utilizing structured and unconventional unstructured knowledge source](https://mlsanity.com/paper/2206.10318/) (2022)

## Abstract (arXiv)

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%.