CoCR-RAG: Enhancing Retrieval-Augmented Generation in Web Q&A via Concept-oriented Context Reconstruction

Abstract

Retrieval-augmented generation (RAG) has shown promising results in enhancing Q&A by incorporating information from the web and other external sources. However, the supporting documents retrieved from the heterogeneous web often originate from multiple sources with diverse writing styles, varying formats, and inconsistent granularity. Fusing such multi-source documents into a coherent and knowledge-intensive context remains a significant challenge, as the presence of irrelevant and redundant information can compromise the factual consistency of the inferred answers. This paper proposes the Concept-oriented Context Reconstruction RAG (CoCR-RAG), a framework that addresses the multi-source information fusion problem in RAG through linguistically grounded concept-level integration. Specifically, we introduce a concept distillation algorithm that extracts essential concepts from Abstract Meaning Representation (AMR), a stable semantic representation that structures the meaning of texts as logical graphs. The distilled concepts from multiple retrieved documents are then fused and reconstructed into a unified, information-intensive context by Large Language Models, which supplement only the necessary sentence elements to highlight the core knowledge. Experiments on the PopQA and EntityQuestions datasets demonstrate that CoCR-RAG significantly outperforms existing context-reconstruction methods across these Web Q&A benchmarks. Furthermore, CoCR-RAG shows robustness across various backbone LLMs, establishing itself as a flexible, plug-and-play component adaptable to different RAG frameworks.

Figures (5)

• Figure 1: Example of RAG based on the concept-oriented reconstructed context.
• Figure 2: The overview of the Concept-oriented Context Reconstruction RAG framework, which consists of three components: (a) information retrieval, (b) context reconstruction, and (c) reconstructed context-based inference. The different arrow colors indicate the corresponding components.
• Figure 3: The evaluation results depict trends in $Acc. \uparrow$ and $AUC\uparrow$ on the PopQA dataset. The vertical axis denotes $Acc$, while the horizontal axis represents the number of supporting documents, $K$. The polyline illustrates the fluctuation in $Acc$ with varying $K$. The shaded area represents $AUC$
• Figure 4: The evaluation results illustrate trends in $Acc. \uparrow$ and $AUC\uparrow$ on the EntityQuestion dataset, following the same axis and symbol definitions as in Fig. \ref{['fig:PopQA']}.
• Figure A1: The node order traversed by DFS($\cdot$) in the AMR graph, where the nodes marked here are not the final distilled concepts.