A Chinese financial event knowledge graph-based retrieval-augmented generation framework for financial question answering
Year: 2026
Type: article
Abstract: 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.
Authors: Haitao Cheng, Ke Wang, Qi Wang, Tao Liu, Kai Sheng
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,200
Award: G6672977052
OpenAlex ID: W7147427193