Ontology-Guided, Hybrid Prompt Learning for Generalization in Knowledge Graph Question Answering

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Authors

Most existing Knowledge Graph Question Answering (KGQA) approaches are designed for a specific KG, such as Wikidata, DBpedia or Freebase. Due to the heterogeneity of the underlying graph schema, topology and assertions, most KGQA systems cannot be transferred to unseen Knowledge Graphs (KGs) without resource-intensive training data. We present OntoSCPrompt, a novel Large Language Model (LLM)-based KGQA approach with a two-stage architecture that separates semantic parsing from KG-dependent interactions. OntoSCPrompt first generates a SPARQL query structure (including SPARQL keywords such as SELECT, ASK, WHERE and placeholders for missing tokens) and then fills them with KG-specific information. To enhance the understanding of the underlying KG, we present an ontology-guided, hybrid prompt learning strategy that integrates KG ontology into the learning process of hybrid prompts (e.g., discrete and continuous vectors). We also present several task-specific decoding strategies to ensure the correctness and executability of generated SPARQL queries in both stages. Experimental results demonstrate that OntoSCPrompt performs as well as SOTA approaches without retraining on a number of KGQA datasets such as CWQ, WebQSP and LC-QuAD 1.0 in a resource-efficient manner and can generalize well to unseen domain-specific KGs like DBLP-QuAD and CoyPu KG 11Code: https://github.com/LongquanJiang/OntoSCPrompt.

OriginalspracheEnglisch
TitelProceedings - 2025 19th International Conference on Semantic Computing, ICSC 2025
Anzahl der Seiten8
VerlagInstitute of Electrical and Electronics Engineers Inc.
Erscheinungsdatum2025
Seiten28-35
ISBN (Print)979-8-3315-2427-2
ISBN (elektronisch)979-8-3315-2426-5
DOIs
PublikationsstatusErschienen - 2025
Veranstaltung19th International Conference on Semantic Computing - ICSC 2025 - Hybrid, Laguna Hills, USA / Vereinigte Staaten
Dauer: 03.02.202505.02.2025
Konferenznummer: 19

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© 2025 IEEE.

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