interdisciplinary-discovery

name	interdisciplinary-discovery
description	Discover interdisciplinary research connections using knowledge graph analysis (PageRank, Louvain, vector similarity). Use when analyzing cross-domain research, finding unexpected connections, or exploring interdisciplinary patterns. Keywords: 跨学科发现, interdisciplinary discovery, kg analysis, 知识图谱分析, find research connections, discover cross-domain patterns.

Interdisciplinary Discovery

Discover unexpected research connections across disciplines using knowledge graph analysis.

Activation Keywords

• 跨学科发现
• interdisciplinary discovery
• 知识图谱分析
• kg analysis
• 找研究关联
• discover cross-domain patterns
• interdisciplinary research
• 知识图谱发现

Tools Used

• exec: Run kg_tool CLI commands
• sqlite3: Direct database queries
• web_search: Search related papers
• read: Load skill references
• write: Save analysis results

Quick Workflow

1. Identify Important Nodes

kg_tool pagerank kg.db

Returns top entities by influence.

2. Find Research Communities

kg_tool louvain kg.db

Detects clusters of related research.

3. Discover Strong Connections

kg_tool similar kg.db <entity_id> 5

Finds entities with high vector similarity (>0.8 = strong connection).

4. Analyze Patterns

• PageRank: Domain influence
• Louvain: Research clusters
• Vector similarity: Cross-domain bridges

5. Extract Insights

Document findings in memory/YYYY-MM-DD.md.

When to Use

Trigger signals:

• User asks "find connections between X and Y"
• Need to explore interdisciplinary patterns
• Research synthesis tasks
• Knowledge graph exploration

Typical patterns:

• Quantum finance connections (0.87 similarity)
• Neuroscience-quantum bridges
• AI-physics intersections

References

• kg_tool usage: See references/kg-tool-guide.md
• Analysis patterns: See references/analysis-patterns.md

Best Practices

1. Start with PageRank - Find influential nodes first
2. Check multiple entities - Don't rely on single similarity search
3. Threshold >0.8 - Strong connections typically have >0.8 similarity
4. Cross-reference communities - Compare Louvain results with similarity
5. Document patterns - Always save to memory/

Examples

Example: Quantum Finance Discovery

kg_tool pagerank kg.db
# Entity 343: Quantum Algorithms (0.045)

kg_tool similar kg.db 128 5
# quantum economics (0.8764)
# Quantum Computing for Finance (0.8445)

# Insight: Strong quantum-finance connection discovered

Example: Neuroscience-Quantum Bridge

kg_tool similar kg.db 9 5
# Brain connectivity tools (0.1522)
# Spiking neural networks (0.1413)

# Insight: Quantum cryptography linked to neuroscience

Integration with Research Workflow

This skill integrates with:

• arxiv-search: Find papers to add to KG
• skill-extractor: Extract patterns from discoveries
• weekly_topics.py: Daily research automation

Limitations

• Requires kg.db with vectors (640+ embeddings)
• Similarity search needs entity with existing vector
• Database may be locked during concurrent writes

Notes

• Part of hourly research automation workflow
• Works best with sqlite-knowledge-graph
• Use proxy for arXiv searches: http://127.0.0.1:7890