# Bridging the Knowledge Gap: Enhancing Question Answering with World and Domain Knowledge

arXiv: https://arxiv.org/abs/1910.07429 · HTML: https://mlsanity.com/paper/1910.07429/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2019.

## Built on by (most cited first, 2 of 2)

- [Reasoning over Vision and Language: Exploring the Benefits of Supplemental Knowledge](https://mlsanity.com/paper/2101.06013/) (2021)
- [Improving Commonsense Causal Reasoning by Adversarial Training and Data Augmentation](https://mlsanity.com/paper/2101.04966/) (2021)

## Abstract (arXiv)

In this paper we present OSCAR (Ontology-based Semantic Composition Augmented Regularization), a method for injecting task-agnostic knowledge from an Ontology or knowledge graph into a neural network during pretraining. We evaluated the impact of including OSCAR when pretraining BERT with Wikipedia articles by measuring the performance when fine-tuning on two question answering tasks involving world knowledge and causal reasoning and one requiring domain (healthcare) knowledge and obtained 33:3%, 18:6%, and 4% improved accuracy compared to pretraining BERT without OSCAR and obtaining new state-of-the-art results on two of the tasks.