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

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

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.

Original languageEnglish
Title of host publicationProceedings - 2025 19th International Conference on Semantic Computing, ICSC 2025
Number of pages8
PublisherInstitute of Electrical and Electronics Engineers Inc.
Publication date2025
Pages28-35
ISBN (print)979-8-3315-2427-2
ISBN (electronic)979-8-3315-2426-5
DOIs
Publication statusPublished - 2025
Event19th International Conference on Semantic Computing - ICSC 2025 - Hybrid, Laguna Hills, United States
Duration: 03.02.202505.02.2025
Conference number: 19

Bibliographical note

Publisher Copyright:
© 2025 IEEE.

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