iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering

TL;DR

iQUEST is a novel question-guided framework that enhances knowledge base question answering by iteratively decomposing complex queries and leveraging graph neural networks for improved multi-hop reasoning over knowledge graphs.

Contribution

The paper introduces iQUEST, a new framework that combines iterative query decomposition with GNN-based lookahead to improve multi-hop reasoning in KBQA tasks.

Findings

Consistent performance improvements across four benchmark datasets.

Effective handling of complex, multi-hop queries.

Enhanced reasoning coherence and path exploration.

Abstract

Large Language Models (LLMs) excel in many natural language processing tasks but often exhibit factual inconsistencies in knowledge-intensive settings. Integrating external knowledge resources, particularly knowledge graphs (KGs), provides a transparent and updatable foundation for more reliable reasoning. Knowledge Base Question Answering (KBQA), which queries and reasons over KGs, is central to this effort, especially for complex, multi-hop queries. However, multi-hop reasoning poses two key challenges: (1)~maintaining coherent reasoning paths, and (2)~avoiding prematurely discarding critical multi-hop connections. To tackle these challenges, we introduce iQUEST, a question-guided KBQA framework that iteratively decomposes complex queries into simpler sub-questions, ensuring a structured and focused reasoning trajectory. Additionally, we integrate a Graph Neural Network (GNN) to look ahead and incorporate 2-hop neighbor information at each reasoning step. This dual approach strengthens the reasoning process, enabling the model to explore viable paths more effectively. Detailed experiments demonstrate the consistent improvement delivered by iQUEST across four benchmark datasets and four LLMs. The code is publicly available at: https://github.com/Wangshuaiia/iQUEST.