用实体摘要提升知识图谱问答，解决多跳推理难题。
MDER-DR: Multi-Hop Question Answering with Entity-Centric Summaries
- 通过实体中心摘要构建索引，避免丢失上下文信息
- 在标准与领域数据集上相比基线提升最高66%
- 适合处理稀疏、不完整关系数据的多跳问答场景
基于知识图谱的检索增强生成（RAG）在将文本压缩为三元组时易丢失重要上下文，导致多跳问答性能下降。我们提出无需领域限定的KG问答框架MDER-DR，其中新索引方法MDER生成上下文相关的三元组描述，并融合实体级摘要，使问答阶段无需显式遍历图边。同时引入分解-求解（DR）检索机制，将用户查询分解为可求解三元组，并通过迭代推理在知识图谱中定位。该框架由大模型驱动，对稀疏、不完整及复杂关系数据具有鲁棒性。实验表明，在标准和特定领域基准上，其性能较标准RAG基线提升最高达66%，且具备跨语言鲁棒性。代码已开源。
原文摘要 · Abstract (English)
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 https://github.com/DataSciencePolimi/MDER-DR_RAG.
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