CompKBQA: Component-wise Task Decomposition for Knowledge Base Question Answering
Yuhang Tian, Dandan Song, Zhijing Wu, Pan Yang, Changzhi Zhou, Jun Yang, Hao Wang, Huipeng Ma
Abstract
Knowledge Base Question Answering (KBQA) aims to extract accurate answers from the Knowledge Base (KB). Traditional Semantic Parsing (SP)-based methods are widely used but struggle with complex queries. Recently, large language models (LLMs) have shown promise in improving KBQA performance. However, the challenge of generating error-free logical forms remains, as skeleton, topic Entity, and relation Errors still frequently occur. To address these challenges, we propose CompKBQA(Component-wise Task Decomposition for Knowledge Base Question Answering), a novel framework that optimizes the process of fine-tuning a LLM for generating logical forms by enabling the LLM to progressively learn relevant sub-tasks like skeleton generation, topic entity generation, and relevant relations generation. Additionally, we propose R 3 , which retrieves and incorporates KB information into the process of logical form generation. Experimental evaluations on two benchmark KBQA datasets, WebQSP and CWQ, demonstrate that CompKBQA achieves state-of-the-art performance, highlighting the importance of task decomposition and KB-aware learning.
BibTeX
@inproceedings{emnlp2025_compkbqacomponen,
title = {CompKBQA: Component-wise Task Decomposition for Knowledge Base Question Answering},
author = {Yuhang Tian and Dandan Song and Zhijing Wu and Pan Yang and Changzhi Zhou and Jun Yang and Hao Wang and Huipeng Ma and Chenhao Li and Luan Zhang},
booktitle = {EMNLP 2025},
year = {2025}
}