GraphER: An Efficient Graph-Based Enrichment and Reranking Method for Retrieval-Augmented Generation

2026-03-25

AI Summary

This work addresses the challenge that existing retrieval-augmented generation systems struggle to effectively aggregate dispersed evidence from multiple sources when handling complex queries, while approaches relying on explicit knowledge graphs suffer from high construction costs and poor compatibility. To overcome these limitations, the authors propose a graph-structured augmentation and reranking method that avoids building a full knowledge graph. During offline preprocessing, data objects are enriched with graph-based contextual information; at inference time, candidate results are reranked using graph-aware proximity measures. The approach is retrieval-agnostic, seamlessly integrates with mainstream vector databases, and significantly improves retrieval performance across multiple benchmarks—while maintaining low inference latency and strong system compatibility.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Machine Learning: Graph-based Machine Learning
• Search and Optimization: Distributed Search

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

Semantic search in retrieval-augmented generation (RAG) systems is often insufficient for complex information needs, particularly when relevant evidence is scattered across multiple sources. Prior approaches to this problem include agentic retrieval strategies, which expand the semantic search space by generating additional queries. However, these methods do not fully leverage the organizational structure of the data and instead rely on iterative exploration, which can lead to inefficient retrieval. Another class of approaches employs knowledge graphs to model non-semantic relationships through graph edges. Although effective in capturing richer proximities, such methods incur significant maintenance costs and are often incompatible with the vector stores used in most production systems. To address these limitations, we propose GraphER, a graph-based enrichment and reranking method that captures multiple forms of proximity beyond semantic similarity. GraphER independently enriches data objects during offline indexing and performs graph-based reranking over candidate objects at query time. This design does not require a knowledge graph, allowing GraphER to integrate seamlessly with standard vector stores. In addition, GraphER is retriever-agnostic and introduces negligible latency overhead. Experiments on multiple retrieval benchmarks demonstrate the effectiveness of the proposed approach.

Problem

• retrieval-augmented generation
• semantic search
• knowledge graph
• information retrieval
• complex information needs

Innovation

• Graph-based Reranking
• Retrieval-Augmented Generation
• Knowledge Graph-Free
• Vector Store Integration
• Proximity Modeling