# Knowledge Fusion and Semantic Knowledge Ranking for Open Domain Question Answering

arXiv: https://arxiv.org/abs/2004.03101 · HTML: https://mlsanity.com/paper/2004.03101/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2020.

## Built on by (most cited first, 9 of 9)

- [QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering](https://mlsanity.com/paper/2104.06378/) (2021)
- [JointLK: Joint Reasoning with Language Models and Knowledge Graphs for Commonsense Question Answering](https://mlsanity.com/paper/2112.02732/) (2021)
- [GNN is a Counter? Revisiting GNN for Question Answering](https://mlsanity.com/paper/2110.03192/) (2021)
- [Leveraging the Inductive Bias of Large Language Models for Abstract Textual Reasoning](https://mlsanity.com/paper/2110.02370/) (2021)
- [Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering](https://mlsanity.com/paper/2105.11776/) (2021)
- [Exploiting Reasoning Chains for Multi-hop Science Question Answering](https://mlsanity.com/paper/2109.02905/) (2021)
- [Best of Both Worlds: A Hybrid Approach for Multi-Hop Explanation with Declarative Facts](https://mlsanity.com/paper/2201.02740/) (2021)
- [Neural Retriever and Go Beyond: A Thesis Proposal](https://mlsanity.com/paper/2205.16005/) (2022)
- [UFO: Unified Fact Obtaining for Commonsense Question Answering](https://mlsanity.com/paper/2305.16048/) (2023)

## Abstract (arXiv)

Open Domain Question Answering requires systems to retrieve external knowledge and perform multi-hop reasoning by composing knowledge spread over multiple sentences. In the recently introduced open domain question answering challenge datasets, QASC and OpenBookQA, we need to perform retrieval of facts and compose facts to correctly answer questions. In our work, we learn a semantic knowledge ranking model to re-rank knowledge retrieved through Lucene based information retrieval systems. We further propose a "knowledge fusion model" which leverages knowledge in BERT-based language models with externally retrieved knowledge and improves the knowledge understanding of the BERT-based language models. On both OpenBookQA and QASC datasets, the knowledge fusion model with semantically re-ranked knowledge outperforms previous attempts.