Leveraging LLMs in Scholarly Knowledge Graph Question Answering

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Authors

This paper presents a scholarly Knowledge Graph Question Answering (KGQA) that answers bibliographic natural language questions by leveraging a large language model (LLM) in a few-shot manner. The model initially identifies the top-n similar training questions related to a given test question via a BERT-based sentence encoder and retrieves their corresponding SPARQL. Using the top-n similar question-SPARQL pairs as an example and the test question creates a prompt. Then pass the prompt to the LLM and generate a SPARQL. Finally, runs the SPARQL against the underlying KG - ORKG (Open Research KG) endpoint and returns an answer. Our system achieves an F1 score of 99.0%, on SciQA - one of the Scholarly-QALD-23 challenge benchmarks.
Original languageEnglish
Title of host publicationJoint Proceedings of Scholarly QALD 2023 and SemREC 2023 co-located with 22nd International Semantic Web Conference ISWC 2023, Athens, Greece, November 6-10, 2023
EditorsDebayan Banerjee, Ricardo Usbeck, Nandana Mihindukulasooriya, Gunjan Singh, Raghava Mutharaju, Pavan Kapanipathi
Number of pages10
Volume3592
PublisherCEUR-WS.org
Publication date2023
DOIs
Publication statusPublished - 2023
EventScholarly QALD 2023 - Athen, Greece
Duration: 06.11.202310.11.2023
Conference number: 1
https://ceur-ws.org/Vol-3592/

Bibliographical note

Publisher Copyright:
© 2023 CEUR-WS. All rights reserved.

    Research areas

  • Informatics - Knowledge Graph Question Answering (KGQA), pen Research Knowledge Graph, arge Language Model, Scholarly KGQA, Scholarly-QALD, RKG, SciQA

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