Common Sense Knowledge Learning for Open Vocabulary Neural Reasoning: A First View into Chronic Disease Literature
Abstract
In this paper, we address reasoning tasks from open vocabulary Knowledge Bases (openKBs) using state-of-the-art Neural Language Models (NLMs) with applications in scientific literature. For this purpose, self-attention based NLMs are trained using a common sense KB as a source task. The NLMs are then tested on a target KB for open vocabulary reasoning tasks involving scientific knowledge related to the most prevalent chronic diseases (also known as non-communicable diseases, NCDs). Our results identified NLMs that performed consistently and with significance in knowledge inference for both source and target tasks. Furthermore, in our analysis by inspection we discussed the semantic regularities and reasoning capabilities learned by the models, while showing a first insight into the potential benefits of our approach to aid NCD research.
Keywords
Cite
@article{arxiv.2111.13781,
title = {Common Sense Knowledge Learning for Open Vocabulary Neural Reasoning: A First View into Chronic Disease Literature},
author = {Ignacio Arroyo-Fernández and José Armando Sánchez-Rojas and Arturo Tellez-Velázquez and Flavio Juárez-Martínez and Raúl Cruz-Barbosa and Enrique Guzmán-Ramírez and Yalbi Itzel Balderas-Martínez},
journal= {arXiv preprint arXiv:2111.13781},
year = {2021}
}