Abstract: Retrieval-augmented generation introduces external knowledge and effectively alleviates the knowledge gaps and hallucination problems of large language models. However, existing methods still face two challenges in complex question-answering scenarios. First, relevant evidence is often scattered across multiple text chunks. Existing retrieval methods have difficulty explicitly modeling the associations among cross-text events. This problem leads to insufficient evidence integration. Second, existing candidate text ranking methods lack sufficient explicit constraints on event-mainline consistency. They tend to rank redundant texts or noisy evidence at the top. Such evidence is superficially relevant but deviates from the target event. This problem affects the accuracy of final answer generation.To address these problems, this paper proposes ED-RAG, an Event-Driven Retrieval-Augmented Generation framework based on a knowledge graph. ED-RAG uses events as the core semantic units for cross-text fact organization and evidence aggregation. It structures event information, entity information, and source relations in unstructured texts. It also explicitly links these elements. This design improves evidence retrieval, integration, and ranking in complex question-answering scenarios.ED-RAG first takes raw text chunks as input. It uses a large language model to jointly extract events and entities. It identifies event names, time, locations, participants, and related entities. It then constructs a lightweight event knowledge graph with text nodes, event nodes, and entity nodes. The graph contains two types of relations: event-source text relations and event-related entity relations. These relations explicitly record event sources and entity constraints. They organize factual information from scattered texts around event semantics.ED-RAG further introduces an event fusion mechanism to handle different natural language expressions of the same event. The mechanism first uses an event vector index to retrieve semantically similar candidate events. It then uses a similarity threshold and shared-entity constraints to perform preliminary filtering. Next, it uses a large language model to determine event identity. The mechanism merges nodes that refer to the same event. It keeps events with key attribute conflicts or insufficient evidence as independent nodes. This strategy reduces the risk of incorrect event merging.In the question-answering stage, ED-RAG extracts entities from the question. It normalizes the entities and evaluates their importance. It selects core entities and maps them to graph nodes. It then retrieves candidate evidence along the “entity-event-text” path. This process enables the retrieved evidence to cover key entities in the question. Event semantics also constrain the retrieved evidence. This design improves the match between the evidence and the question intent. When graph-path retrieval fails, the system uses the event vector index to retrieve the events that are semantically closest to the question. It then traces these events back to their source texts and uses the texts as fallback evidence.Finally, ED-RAG integrates multiple signals, including entity coverage, question-text semantic relevance, and question-event semantic consistency. It uses these signals to rerank candidate evidence. It then inputs high-quality text chunks into a large language model to generate the answer.This paper conducts experiments on four datasets: QA(1-document), QA(2-document), QA(3-document), and UHGEval. The experimental results show that ED-RAG achieves F1QE scores of 63.05 and 57.08 on the cross-text question-answering datasets QA(2-document) and QA(3-document), respectively. Compared with the best baseline method, ED-RAG improves F1QE by 7.34 and 4.25 points on the two datasets, respectively. The experimental results show that ED-RAG has stronger advantages in cross-document evidence localization and integration. Events serve as intermediate semantic units for cross-text evidence organization. This design enhances the aggregation of scattered evidence and strengthens event-mainline constraints. The ranking strategy integrates entity, text-semantic, and event-consistency signals. It effectively suppresses noise interference and provides more reliable context for large language models. Therefore, ED-RAG improves the accuracy of answer generation in complex question-answering scenarios.

摘要： 检索增强生成通过引入外部知识能够有效缓解大语言模型的知识缺失与幻觉问题，但现有方法在复杂问答场景中仍面临两方面挑战：一是相关证据往往分散于多个文本片段中，现有检索方式难以显式刻画跨文本事件之间的关联，导致证据整合不足；二是现有候选文本排序对事件主线一致性的显式约束仍不充分，容易将表层相关但事件指向偏离的冗余文本或噪声证据排在前列，进而影响最终答案生成的准确性。针对上述问题，本文提出一种基于事件驱动知识图谱的检索增强生成框架（ED-RAG）。该框架以事件作为跨文本事实组织与证据聚合的核心语义单元，将非结构化文本中的事件信息、实体信息及其来源关系进行结构化组织与显式关联，从而提升复杂问答场景下的证据召回、融合与排序能力。ED-RAG首先以原始文本块为输入，利用大语言模型进行事件—实体联合抽取，识别事件名称、时间、地点、参与对象及相关实体等要素，并构建由文本节点、事件节点和实体节点组成的轻量级事件知识图谱。图谱通过“事件—来源文本”和“事件—相关实体”两类关系显式记录事件来源与实体约束，使分散文本中的事实信息能够围绕事件语义进行组织。针对同一事件存在多种自然语言表述的问题，ED-RAG进一步引入事件融合机制：（1）基于事件向量索引检索语义相近的候选事件，并结合相似度阈值和共享实体约束进行初步筛选；（2）利用大语言模型判断事件同一性。对判定为同一事件的节点进行归并；对存在关键属性冲突或判断依据不足的事件，则保留为独立节点，以降低事件误合并的风险。在问答阶段，ED-RAG对问题中的实体进行抽取、规范化和重要性评估，选取核心实体并映射至图谱节点，沿“实体—事件—文本”路径召回候选证据。该过程使召回结果既覆盖问题关键实体，又受到事件语义约束，从而提升证据与问题意图的匹配程度。当图谱路径召回失败时，系统基于事件向量索引检索与问题语义最接近的事件，并回溯其来源文本作为兜底证据。最后，ED-RAG采用融合实体覆盖度、问题—文本语义相关性及问题—事件语义一致性等多源信号的排序机制，对候选证据进行重排序，并将高质量文本片段输入大语言模型生成答案。为评估所提方法的有效性，本文在QA(1-document)、QA(2-document)、QA(3-document)和UHGEval四个数据集上开展实验。实验结果表明，ED-RAG在跨文本问答数据集QA(2-document)和QA(3-document)上的F1QE分别达到63.05和57.08，较最优基线方法分别提升7.34和4.25。实验结果表明，ED-RAG在跨文档证据定位和整合方面更具优势，以事件作为跨文本证据组织的中间语义单元，能够增强分散证据的聚合能力，并强化事件主线约束；融合实体、文本语义和事件一致性信号的排序策略能够有效抑制噪声干扰，为大语言模型提供更可靠的上下文支撑，从而提升复杂问答场景下答案生成的准确性。