Year: 2025
Type: conference-paper
Abstract: Most existing Knowledge Base Question Answering methods focus primarily on retrieving factual information, leaving more complex, analysis-driven tasks relatively unexplored. However, real-world queries often involve graph-based computations such as degree calculation or community detection, which require more advanced reasoning. In this paper, we introduce LLM4GraphAna, a Large Language Model-based approach designed to handle these challenging, analysis-focused queries within the KBQA framework. By integrating Function Orchestration and Parameterization, LLM4GraphAna can invoke our well-defined functions to perform graph analytics. Experimental results demonstrate that our method significantly improves performance on analysis-intensive questions.
Authors: Hao Chen, Lun Du, Xu Chen, Xiaojun Ma, Jiang Zhang
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4411549723