Au sujet de ce document
- sl:arxiv_author :
- sl:arxiv_firstAuthor : Ankush Agarwal
- sl:arxiv_num : 2205.15952
- sl:arxiv_published : 2022-05-31T16:49:55Z
- sl:arxiv_summary : 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.@en
- sl:arxiv_title : Knowledge Graph -- Deep Learning: A Case Study in Question Answering in Aviation Safety Domain@en
- sl:arxiv_updated : 2022-05-31T16:49:55Z
- sl:bookmarkOf : https://arxiv.org/abs/2205.15952
- sl:creationDate : 2022-06-11
- sl:creationTime : 2022-06-11T01:48:52Z
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