# Bubblerag Evidence Driven Graphs

> 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.

- Skill: `adu2021/bubblerag-evidence-driven-graphs` (Agent Skill)
- Install (CLI): `npx skillmds@latest add adu2021/bubblerag-evidence-driven-graphs`
- Raw SKILL.md: https://api.skillmd.com/api/skills/adu2021/bubblerag-evidence-driven-graphs/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: MIT
- Author: adu2021 (https://skillmd.com/u/adu2021)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/adu2021/bubblerag-evidence-driven-graphs

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## 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