iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering
Shuai Wang, Yinan Yu

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
MethodsBalanced Selection · Graph Neural Network
