TY - CHAP

T1 - Ontology-Guided, Hybrid Prompt Learning for Generalization in Knowledge Graph Question Answering

AU - Jiang, Longquan

AU - Huang, Junbo

AU - Moller, Cedric

AU - Usbeck, Ricardo

N1 - Conference code: 19

PY - 2025

Y1 - 2025

N2 - 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.

AB - 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.

KW - Generalization

KW - KGQA

KW - LLM

KW - QA

KW - Informatics

UR - http://www.scopus.com/inward/record.url?scp=105009456479&partnerID=8YFLogxK

U2 - 10.48550/arXiv.2502.03992

DO - 10.48550/arXiv.2502.03992

M3 - Article in conference proceedings

SN - 979-8-3315-2427-2

T3 - Proceedings - IEEE International Conference on Semantic Computing, ICSC

SP - 28

EP - 35

BT - Proceedings - 2025 19th International Conference on Semantic Computing, ICSC 2025

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 19th International Conference on Semantic Computing - ICSC 2025

Y2 - 3 February 2025 through 5 February 2025

ER -