Leveraging LLMs in Scholarly Knowledge Graph Question Answering

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet


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.
TitelJoint Proceedings of Scholarly QALD 2023 and SemREC 2023 co-located with 22nd International Semantic Web Conference ISWC 2023, Athens, Greece, November 6-10, 2023
HerausgeberDebayan Banerjee, Ricardo Usbeck, Nandana Mihindukulasooriya, Gunjan Singh, Raghava Mutharaju, Pavan Kapanipathi
Anzahl der Seiten10
PublikationsstatusErschienen - 2023
VeranstaltungScholarly QALD 2023 - Athen, Griechenland
Dauer: 06.11.202310.11.2023
Konferenznummer: 1

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