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Question Answering over Linked Data with GPT-3

Authors Bruno Faria , Dylan Perdigão , Hugo Gonçalo Oliveira

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Author Details

Bruno Faria
  • Department of Informatics Engineering, University of Coimbra, Portugal
  • Centre for Informatics and Systems of the University of Coimbra, Portugal
Dylan Perdigão
  • Department of Informatics Engineering, University of Coimbra, Portugal
  • Centre for Informatics and Systems of the University of Coimbra, Portugal
Hugo Gonçalo Oliveira
  • Department of Informatics Engineering, University of Coimbra, Portugal
  • Centre for Informatics and Systems of the University of Coimbra, Portugal

Funding

This work was partially supported by the Portuguese Recovery and Resilience Plan (PRR) through project C645008882-00000055, Center for Responsible AI; and by FCT - Foundation for Science and Technology, I.P., within the scope of the project CISUC - UID/CEC/00326/2020 and by the European Social Fund, through the Regional Operational Program Centro 2020. It is also based upon work in COST Action CA18209 Nexus Linguarum, supported by COST (European Cooperation in Science and Technology). {http://www.cost.eu/}.

Cite As Get BibTex

Bruno Faria, Dylan Perdigão, and Hugo Gonçalo Oliveira. Question Answering over Linked Data with GPT-3. In 12th Symposium on Languages, Applications and Technologies (SLATE 2023). Open Access Series in Informatics (OASIcs), Volume 113, pp. 1:1-1:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/OASIcs.SLATE.2023.1

Abstract

This paper explores GPT-3 for answering natural language questions over Linked Data. Different engines of the model and different approaches are adopted for answering questions in the QALD-9 dataset, namely: zero and few-shot SPARQL generation, as well as fine-tuning in the training portion of the dataset. Answers retrieved by the generated queries and answers generated directly by the model are also compared. Overall results are generally poor, but several insights are provided on using GPT-3 for the proposed task.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Natural language processing
Keywords
  • SPARQL Generation
  • Prompt Engineering
  • Few-Shot Learning
  • Question Answering
  • GPT-3

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References

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