# Knowledge Graph - Deep Learning: A Case Study in Question Answering in Aviation Safety Domain

arXiv: https://arxiv.org/abs/2205.15952 · HTML: https://mlsanity.com/paper/2205.15952/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 31 May 2022.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning](https://mlsanity.com/paper/1709.00103/) (2017)
- [A Comparative Survey of Recent Natural Language Interfaces for Databases](https://mlsanity.com/paper/1906.08990/) (2019)
- [X-SQL: reinforce schema representation with context](https://mlsanity.com/paper/1908.08113/) (2019)
- [Towards a Question Answering System over the Semantic Web](https://mlsanity.com/paper/1803.00832/) (2018)
- [A question-answering system for aircraft pilots' documentation](https://mlsanity.com/paper/2011.13284/) (2020)

## Built on by (most cited first, 5 of 5)

- [Applications of natural language processing in aviation safety: A review and qualitative analysis](https://mlsanity.com/paper/2501.06210/) (2025)
- [There is No Big Brother or Small Brother: Knowledge Infusion in Language Models for Link Prediction and Question Answering](https://mlsanity.com/paper/2301.04013/) (2023)
- [KITLM: Domain-Specific Knowledge InTegration into Language Models for Question Answering](https://mlsanity.com/paper/2308.03638/) (2023)
- [OMoS-QA: A Dataset for Cross-Lingual Extractive Question Answering in a German Migration Context](https://mlsanity.com/paper/2407.15736/) (2024)
- [Hierarchical Multi-label Classification for Fine-level Event Extraction from Aviation Accident Reports](https://mlsanity.com/paper/2403.17914/) (2024)

## Abstract (arXiv)

In the commercial aviation domain, there are a large number of documents, like, accident reports (NTSB, ASRS) and regulatory directives (ADs). There is a need for a system to access these diverse repositories efficiently in order to service needs in the aviation industry, like maintenance, compliance, and safety. In this paper, we propose a Knowledge Graph (KG) guided Deep Learning (DL) based Question Answering (QA) system for aviation safety. We construct a Knowledge Graph from Aircraft Accident reports and contribute this resource to the community of researchers. The efficacy of this resource is tested and proved by the aforesaid QA system. Natural Language Queries constructed from the documents mentioned above are converted into SPARQL (the interface language of the RDF graph database) queries and answered. On the DL side, we have two different QA models: (i) BERT QA which is a pipeline of Passage Retrieval (Sentence-BERT based) and Question Answering (BERT based), and (ii) the recently released GPT-3. We evaluate our system on a set of queries created from the accident reports. Our combined QA system achieves 9.3% increase in accuracy over GPT-3 and 40.3% increase over BERT QA. Thus, we infer that KG-DL performs better than either singly.