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

• URL: http://arxiv.org/abs/2205.15952v1
• Date: Tue, 31 May 2022 16:49:55 GMT
• Title: Knowledge Graph -- Deep Learning: A Case Study in Question Answering in Aviation Safety Domain
• Authors: Ankush Agarwal, Raj Gite, Shreya Laddha, Pushpak Bhattacharyya, Satyanarayan Kar, Asif Ekbal, Prabhjit Thind, Rajesh Zele, Ravi Shankar
• Abstract summary: 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. Our combined QA system achieves 9.3% increase in accuracy over GPT-3 and 40.3% increase over BERT QA.
• Score: 37.73707014280823
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Customized Retrieval Augmented Generation and Benchmarking for EDA Tool Documentation QA [5.0108982850526]
  Retrieval augmented generation (RAG) enhances the accuracy and reliability of generative AI models by sourcing factual information from external databases. This paper proposes a customized RAG framework along with three domain-specific techniques for EDA tool documentation QA. We have developed and released a documentation QA evaluation benchmark, ORD-QA, for OpenROAD, an advanced RTL-to-GDSII design platform.
  arXiv  Detail & Related papers  (2024-07-22T03:44:27Z)
• EWEK-QA: Enhanced Web and Efficient Knowledge Graph Retrieval for Citation-based Question Answering Systems [103.91826112815384]
  citation-based QA systems are suffering from two shortcomings. They usually rely only on web as a source of extracted knowledge and adding other external knowledge sources can hamper the efficiency of the system. We propose our enhanced web and efficient knowledge graph (KG) retrieval solution (EWEK-QA) to enrich the content of the extracted knowledge fed to the system.
  arXiv  Detail & Related papers  (2024-06-14T19:40:38Z)
• Building Efficient and Effective OpenQA Systems for Low-Resource Languages [17.64851283209797]
  We show that effective, low-cost OpenQA systems can be developed for low-resource contexts. Key ingredients are weak supervision using machine-translated labeled datasets and a relevant unstructured knowledge source. We present SQuAD-TR, a machine translation of SQuAD2.0, and we build our OpenQA system by adapting ColBERT-QA and retraining it over Turkish resources.
  arXiv  Detail & Related papers  (2024-01-07T22:11:36Z)
• RealTime QA: What's the Answer Right Now? [137.04039209995932]
  We introduce REALTIME QA, a dynamic question answering (QA) platform that announces questions and evaluates systems on a regular basis. We build strong baseline models upon large pretrained language models, including GPT-3 and T5. GPT-3 tends to return outdated answers when retrieved documents do not provide sufficient information to find an answer.
  arXiv  Detail & Related papers  (2022-07-27T07:26:01Z)
• Relation-Guided Pre-Training for Open-Domain Question Answering [67.86958978322188]
  We propose a Relation-Guided Pre-Training (RGPT-QA) framework to solve complex open-domain questions. We show that RGPT-QA achieves 2.2%, 2.4%, and 6.3% absolute improvement in Exact Match accuracy on Natural Questions, TriviaQA, and WebQuestions.
  arXiv  Detail & Related papers  (2021-09-21T17:59:31Z)
• Improving Unsupervised Question Answering via Summarization-Informed Question Generation [47.96911338198302]
  Question Generation (QG) is the task of generating a plausible question for a passage, answer> pair. We make use of freely available news summary data, transforming declarative sentences into appropriate questions using dependency parsing, named entity recognition and semantic role labeling. The resulting questions are then combined with the original news articles to train an end-to-end neural QG model.
  arXiv  Detail & Related papers  (2021-09-16T13:08:43Z)
• A question-answering system for aircraft pilots' documentation [58.720142291102135]
  The aerospace industry relies on massive collections of complex and technical documents covering system descriptions, manuals or procedures. This paper presents a question answering (QA) system that would help aircraft pilots access information by naturally interacting with the system and asking questions in natural language.
  arXiv  Detail & Related papers  (2020-11-26T13:33:47Z)
• Harvesting and Refining Question-Answer Pairs for Unsupervised QA [95.9105154311491]
  We introduce two approaches to improve unsupervised Question Answering (QA) First, we harvest lexically and syntactically divergent questions from Wikipedia to automatically construct a corpus of question-answer pairs (named as RefQA) Second, we take advantage of the QA model to extract more appropriate answers, which iteratively refines data over RefQA.
  arXiv  Detail & Related papers  (2020-05-06T15:56:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.