mlsanityData to 7 Oct 2026

Comparative Analysis of Neural Retriever-Reranker Pipelines for Retrieval-Augmented Generation over Knowledge Graphs in E-commerce Applications

arXiv 2602.22219 ↗First posted 14 Dec 2025cs.IR cs.AI cs.CLcited by 0

On the map: Retrieval-augmented generation → Knowledge-graph and structured-knowledge RAG

Abstract (arXiv)

Recent advancements in Large Language Models (LLMs) have transformed Natural Language Processing (NLP), enabling complex information retrieval and generation tasks. Retrieval-Augmented Generation (RAG) has emerged as a key innovation, enhancing factual accuracy and contextual grounding by integrating external knowledge sources with generative models. Although RAG demonstrates strong performance on unstructured text, its application to structured knowledge graphs presents challenges: scaling retrieval across connected graphs and preserving contextual relationships during response generation. Cross-encoders refine retrieval precision, yet their integration with structured data remains underexplored. Addressing these challenges is crucial for developing domain-specific assistants that operate in production environments. This study presents the design and comparative evaluation of multiple Retriever-Reranker pipelines for knowledge graph natural language queries in e-Commerce contexts. Using the STaRK Semi-structured Knowledge Base (SKB), a production-scale e-Commerce dataset, we evaluate multiple RAG pipeline configurations optimized for language queries. Experimental results demonstrate substantial improvements over published benchmarks, achieving 20.4% higher Hit@1 and 14.5% higher Mean Reciprocal Rank (MRR). These findings establish a practical framework for integrating domain-specific SKBs into generative systems. Our contributions provide actionable insights for the deployment of production-ready RAG systems, with implications that extend beyond e-Commerce to other domains that require information retrieval from structured knowledge bases.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation. Builds on 22 of its 50 references that are in the map. First posted on arXiv 14 Dec 2025.

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22 of 50 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Graph Retrieval-Augmented Generation: A Survey15 Aug
  2. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarization24 Apr

Other references · 20

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  3. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
  4. 2020MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformerscited by 1,414
  5. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
  6. 2017Neural Collaborative Filteringcited by 1,108
  7. 2019Passage Re-ranking with BERTcited by 629
  8. 2020Sparse, Dense, and Attentional Representations for Text Retrievalcited by 213
  9. 2023Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Surveycited by 92
  10. 2024STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Basescited by 53
  11. 2024PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makerscited by 42
  12. 2023DiffKG: Knowledge Graph Diffusion Model for Recommendationcited by 41
8 more
  1. 2024IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologuescited by 40
  2. 2024A Thorough Comparison of Cross-Encoders and LLMs for Reranking SPLADEcited by 18
  3. 2023One Model for All: Large Language Models are Domain-Agnostic Recommendation Systemscited by 17
  4. 2023On the Opportunities and Challenges of Offline Reinforcement Learning for Recommender Systemscited by 10
  5. 2024Understanding the Fundamental Design Decisions of Retrieval-Augmented Generation Systemscited by 7
  6. 2023Efficient Neural Ranking using Forward Indexes and Lightweight Encoderscited by 6
  7. 2022NIR-Prompt: A Multi-task Generalized Neural Information Retrieval Training Frameworkcited by 5
  8. 2024Set-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoderscited by 5

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  1. 2024Retrieval-Augmented Generation for Natural Language Processing: A Survey
  2. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models
  3. 2026UltRAG: a Universal Simple Scalable Recipe for Knowledge Graph RAG
  4. 2024Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation
  5. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generation
  6. 2025A Query-Aware Multi-Path Knowledge Graph Fusion Approach for Enhancing Retrieval-Augmented Generation in Large Language Models
  7. 2025Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey
  8. 2025GRIL: Knowledge Graph Retrieval-Integrated Learning with Large Language Models