A Structure and Content Prompt-based Method for Knowledge Graph Question Answering over Scholarly Data

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Authors

Answering scholarly questions is challenging without the help of query-based systems. Thus, we develop a divide-and-conquer approach based on a Large Language Model (LLM) for scholarly Knowledge Graph (KG) Question Answering (QA). Our system integrates the KG ontology into the LLM prompts and leverages a hybrid prompt learning strategy with both query structure and content. Our experiments suggest that given an ontology of a specific KG, LLMs are capable of automatically choosing the corresponding classes or predicates required to generate a target SPARQL query from a natural language question. Our approach shows state-of-the-art results over one scholarly KGQA dataset, namely sciQA [1].

OriginalspracheEnglisch
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, Mohamad Yaser Jaradeh, Sören Auer, Gunjan Singh, Raghava Mutharaju, Pavan Kapanipathi
Anzahl der Seiten7
Band3592
ErscheinungsortAachen
VerlagRheinisch-Westfälische Technische Hochschule Aachen
Erscheinungsdatum14.12.2023
Aufsatznummer3
PublikationsstatusErschienen - 14.12.2023
VeranstaltungScholarly QALD 2023 - Athen, Griechenland
Dauer: 06.11.202310.11.2023
Konferenznummer: 1
https://ceur-ws.org/Vol-3592/

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