Year: 2025
Type: conference-paper
Abstract: Large Language Models (LLMs) exhibit strong reasoning capabilities in complex tasks.However, they still struggle with hallucinations and factual errors in knowledge-intensive scenarios like knowledge graph question answering (KGQA).We attribute this to the semantic gap between structured knowledge graphs (KGs) and unstructured queries, caused by inherent differences in their focuses and structures.Existing methods usually employ resourceintensive, non-scalable workflows reasoning on vanilla KGs, but overlook this gap.To address this challenge, we propose a flexible framework, Enrich-on-Graph (EoG), which leverages LLMs' prior knowledge to enrich KGs, bridge the semantic gap between graphs and queries.EoG enables efficient evidence extraction from KGs for precise and robust reasoning, while ensuring low computational costs, scalability, and adaptability across different methods.Furthermore, we propose three graph quality evaluation metrics to analyze query-graph alignment in KGQA task, supported by theoretical validation of our optimization objectives.Extensive experiments on two KGQA benchmark datasets indicate that EoG can effectively generate high-quality KGs and achieve the stateof-the-art performance.Our code and data are available at https://github.com/zjukg/ Enrich-on-Graph.
Authors: Songze Li, Zhiqiang Liu, Zhengke Gui, Huajun Chen, Wen Zhang
Institutions: Ant Group (China), Zhejiang University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4416036602