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

Source: [Bridging the Knowledge Gap: Enhancing Question Answering with World and Domain Knowledge](<https://www.sota2.com/research/paper/68fa667d1e209131cea10947>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:51:21.649Z

Authors: Travis R. Goodwin, Dina Demner-Fushman

Published: 2019-10-16T15:44:09.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1910.07429>)

[PDF](<https://arxiv.org/pdf/1910.07429.pdf>)