bubblerag-evidence-driven-graphs

name	bubblerag-evidence-driven-graphs
title	BubbleRAG: Evidence-Driven Retrieval-Augmented Generation for Black-Box Knowledge Graphs
version	0.0.3
engine	skillxiv-v0.0.3-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2603.20309
keywords	["RAG","Knowledge Graphs","Evidence Retrieval","Black-Box Graphs","Hallucination Reduction"]
description	Address hallucinations in LLM QA over black-box knowledge graphs using evidence-driven retrieval. Formalize Optimal Informative Subgraph Retrieval and employ bubble expansion to discover candidate evidence graphs, achieving state-of-the-art multi-hop QA performance.

Component ID

Bubble Expansion Evidence Retrieval for Black-Box Knowledge Graph Subgraph Selection

Motivation

Retrieving relevant subgraphs from knowledge graphs with unknown schema and structure introduces three sources of uncertainty: semantic instantiation uncertainty (mapping entities to graph nodes), structural path uncertainty (finding multi-hop connection patterns), and evidential comparison uncertainty (ranking candidate subgraphs). Existing retrieval approaches fail when graph structure is opaque and require significant prior knowledge.

The Modification

BubbleRAG formalizes the challenge as Optimal Informative Subgraph Retrieval and proposes a training-free pipeline combining:

1. Heuristic Bubble Expansion - Discover candidate evidence graphs (CEGs) through iterative neighborhood expansion around seed entities. Bubbles grow by following graph edges without knowledge of full schema, enabling exploration even with structural uncertainty.

2. Semantic Anchor Grouping - Ground semantic entities in graph nodes despite instantiation uncertainty, clustering related entities and enriching them with cross-references.

3. Composite Ranking - Rank candidate subgraphs using task-aware signals (relevance to question, coverage of reasoning paths) balancing recall and precision.

4. Reasoning-Aware Expansion - Prioritize expansion directions that align with the reasoning structure required for the question (multi-hop reasoning patterns).

Ablation Results

The paper demonstrates:

• State-of-the-art performance on multi-hop question answering tasks
• Effective hallucination reduction through grounded evidence retrieval
• Performance maintained across diverse black-box knowledge graph structures
• Training-free pipeline achieves competitive or superior results to fine-tuned baselines
• Bubble expansion efficiently explores graph neighborhoods without schema knowledge

Conditions

• Applicable to knowledge graphs with unknown or partially-known schema
• Works best when questions require multi-hop reasoning (2+ steps)
• Assumes entity mentions in questions are matchable to some graph node representation
• Training-free design minimizes data requirements but assumes graph is queryable at inference time

Drop-In Checklist

• Implement semantic anchor detection and entity-to-node grounding
• Add bubble expansion algorithm for neighborhood exploration
• Create composite ranking function (relevance + coverage + reasoning alignment)
• Implement reasoning-aware expansion (prioritize task-relevant directions)
• Integrate evidence graph selection with downstream LLM QA
• Test on multi-hop QA benchmarks with black-box graphs
• Validate hallucination reduction compared to baseline RAG