A Chinese financial event knowledge graph-based retrieval-augmented generation framework for financial question answering

Financial question answering in the Chinese domain presents significant challenges due to complex domain-specific terminology and the integration of heterogeneous financial research reports from multiple institutions. To address these issues, we propose a Chinese financial event knowledge graph-based retrieval-augmented generation framework. The framework constructs a structured index via semantic-aware text chunking and large language model-driven triplet extraction, incorporating a generation–verification mechanism to ensure reliable and relevant information retrieval. To mitigate vague or underspecified user queries that commonly occur in Chinese due to implicit expressions and unclear word boundaries, a reinforcement learning-based query reformulation module generates domain-specific representations, improving retrieval intent alignment. A dual-level retrieval mechanism is designed to retrieve core entities via semantic similarity and then expand event chains through knowledge graph-based neighbor expansion. Experimental results across three question types (single-hop, multi-hop, and open-ended) and four evaluation dimensions (comprehensiveness, diversity, empowerment, and overall performance) demonstrate that the proposed framework consistently outperforms baseline models, showing superior performance across various financial question answering tasks.