[2604.04359] GROUNDEDKG-RAG: Grounded Knowledge Graph Index for Long-document Question Answering

Computer Science > Computation and Language arXiv:2604.04359 (cs) [Submitted on 6 Apr 2026] Title:GROUNDEDKG-RAG: Grounded Knowledge Graph Index for Long-document Question Answering Authors:Tianyi Zhang, Andreas Marfurt View a PDF of the paper titled GROUNDEDKG-RAG: Grounded Knowledge Graph Index for Long-document Question Answering, by Tianyi Zhang and 1 other authors View PDF HTML (experimental) Abstract:Retrieval-augmented generation (RAG) systems have been widely adopted in contemporary large language models (LLMs) due to their ability to improve generation quality while reducing the required input context length. In this work, we focus on RAG systems for long-document question answering. Current approaches suffer from a heavy reliance on LLM descriptions resulting in high resource consumption and latency, repetitive content across hierarchical levels, and hallucinations due to no or limited grounding in the source text. To improve both efficiency and factual accuracy through grounding, we propose GroundedKG-RAG, a RAG system in which the knowledge graph is explicitly extracted from and grounded in the source document. Specifically, we define nodes in GroundedKG as entities and actions, and edges as temporal or semantic relations, with each node and edge grounded in the original sentences. We construct GroundedKG from semantic role labeling (SRL) and abstract meaning representation (AMR) parses and then embed it for retrieval. During querying, we apply the same transfo...