Beyond Seen Data: Improving KBQA Generalization Through Schema-Guided Logical Form Generation

ACL ARR 2025 May Submission7489 Authors

Abstract: Knowledge base question answering (KBQA) aims to answer user questions in natural language using rich human knowledge stored in large KBs. As current KBQA methods struggle with unseen knowledge base elements and their novel compositions at test time, we introduce \textbf{SG-KBQA} --- a novel model that injects schema contexts into entity retrieval and logical form generation to tackle this issue. It exploits information about the semantics and structure of the knowledge base provided by schema contexts to enhance generalizability. We show that \model\ achieves strong generalizability, outperforming state-of-the-art models on two commonly used benchmark datasets across a variety of test settings. Our source code is available at \url{https://anonymous.4open.science/r/SG-KBQA-7895}.

Paper Type: Long

Research Area: Question Answering

Research Area Keywords: Knowledge Base Question Answering

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 7489