Abstract
Retrieval-Augmented Generation (RAG) over Knowledge Graphs (KGs) suffers from the fact that indexing approaches may lose important contextual nuance when text is reduced to triples, thereby degrading performance in downstream Question-Answering (QA) tasks, particularly for multi-hop QA, which requires composing answers from multiple entities, facts, or relations. We propose a domain-agnostic, KG-based QA framework that covers both the indexing and retrieval/inference phases. A new indexing approach called Map-Disambiguate-Enrich-Reduce (MDER) generates context-derived triple descriptions and subsequently integrates them with entity-level summaries, thus avoiding the need for explicit traversal of edges in the graph during the QA retrieval phase. Complementing this, we introduce Decompose-Resolve (DR), a retrieval mechanism that decomposes user queries into resolvable triples and grounds them in the KG via iterative reasoning. Together, MDER and DR form an LLM-driven QA pipeline that is robust to sparse, incomplete, and complex relational data. Experiments show that on standard and domain specific benchmarks, MDER-DR achieves substantial improvements over standard RAG baselines (up to 66%), while maintaining cross-lingual robustness. Our code is available at this https URL.
Abstract (translated)
基于知识图谱（KG）的检索增强生成（RAG）面临的一个问题是，索引方法可能在将文本缩减为三元组时丢失重要的上下文细节，从而导致下游问答任务（QA）性能下降，特别是在需要从多个实体、事实或关系中构建答案的多跳问答中。为此，我们提出了一种领域无关的知识图谱基础问答框架，涵盖索引和检索/推理阶段。 我们的方法包括一种新的称为映射-消歧-丰富-简化（MDER）的索引方式，它生成由上下文衍生出的三元组描述，并随后将这些描述与实体级别的摘要集成在一起。这种方法避免了在问答检索阶段显式遍历图中的边的需求。此外，我们还引入了一种称为分解-解析（DR）的检索机制，该机制将用户查询分解为可解析的三元组，并通过迭代推理将其锚定到知识图谱中。 MDER和DR共同形成了一个基于大语言模型的问答流水线，能够应对稀疏、不完整以及复杂的关系数据。实验表明，在标准和领域特定基准上，MDER-DR相比传统的RAG基线方法取得了显著改进（最高可达66%），同时保持了跨语言鲁棒性。 我们的代码可在提供的链接中获得。
URL
https://arxiv.org/abs/2603.11223
