Continuous Graph Refactoring Guide - MECE Schema Evolution

continuous-refactoring-guide.md

Continuous Graph Refactoring Guide

🎯 Your Question Answered: YES!

Q: Do entity types and predicates improve with more data? A: YES! With the new refactoring tools, the graph continuously evolves to be more MECE and generic.

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🔄 Continuous Improvement Workflow

┌─────────────────────────────────────────────────────────────┐
│  EXTRACTION PHASE (Schemes 1-10)                            │
│  • Uses initial entity types & predicates                   │
│  • Discovers patterns: requires_age, requires_income, etc.  │
└─────────────────┬───────────────────────────────────────────┘
                  │
                  ▼
┌─────────────────────────────────────────────────────────────┐
│  ANALYSIS PHASE (After 10 schemes)                          │
│  • analyze_entity_types() → Find underused types            │
│  • analyze_predicates() → Find redundant predicates         │
│  • Get AI suggestions for improvements                      │
└─────────────────┬───────────────────────────────────────────┘
                  │
                  ▼
┌─────────────────────────────────────────────────────────────┐
│  REFACTORING PHASE (Improve schema)                         │
│  • consolidate_predicates() → Merge requires_* → requires   │
│  • rename_entity_type() → age_requirement → requirement     │
│  • rename_predicate() → Normalize naming                    │
└─────────────────┬───────────────────────────────────────────┘
                  │
                  ▼
┌─────────────────────────────────────────────────────────────┐
│  EXTRACTION PHASE (Schemes 11-50)                           │
│  • Uses improved, generic schema                            │
│  • Better entity reuse                                      │
│  • More MECE compliance                                     │
└─────────────────┬───────────────────────────────────────────┘
                  │
                  ▼
              REPEAT CYCLE

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🛠️ Refactoring Tools

1. analyze_entity_types() - Discover Improvement Opportunities

Current Issues Found:

❌ caste (1 entity) - Only 1 entity, consider merging
❌ area_type (1 entity) - Only 1 entity, consider merging
❌ occupation (1 entity) - Only 1 entity, consider merging
💡 Recommendation: Standardize requirement types under "requirement"

What to do:

// Merge underused types into "requirement"
rename_entity_type({
  oldType: "age_requirement",
  newType: "requirement"
})

rename_entity_type({
  oldType: "income_criteria",
  newType: "requirement"
})

rename_entity_type({
  oldType: "caste",
  newType: "requirement"
})

rename_entity_type({
  oldType: "area_type",
  newType: "requirement"
})

// Result: More generic, MECE-compliant types!

---

2. analyze_predicates() - Find Redundant Relationships

Current Issues Found:

❌ 7 different "requires_*" predicates:
   - requires_age (6 uses)
   - requires_income (5 uses)
   - requires_gender (3 uses)
   - requires_caste (3 uses)
   - requires_area (1 use)
   - requires_area_type (1 use)
   - requires_occupation (1 use)

💡 Suggested merge: "requires" (generic)

What to do:

// Consolidate all "requires_*" into generic "requires"
consolidate_predicates({
  predicates: [
    "requires_age",
    "requires_income",
    "requires_gender",
    "requires_caste",
    "requires_area",
    "requires_area_type",
    "requires_occupation"
  ],
  targetPredicate: "requires"
})

// Similarly for "allows_*":
consolidate_predicates({
  predicates: [
    "allows_area_type",
    "allows_caste",
    "allows_gender"
  ],
  targetPredicate: "allows"
})

// Result:
// Before: scheme requires_age age_min_40
// After:  scheme requires requirement:age_min_40
//         (Entity type provides context, predicate is generic!)

---

3. rename_entity_type() - Standardize Entity Types

Example: Merge all requirement types

// Before refactoring:
Entity Types:
  - age_requirement (4 entities)
  - income_criteria (4 entities)
  - caste (1 entity)
  - area_type (1 entity)
  - occupation (1 entity)
Total: 5 types, 11 entities

// Refactoring:
rename_entity_type({ oldType: "age_requirement", newType: "requirement" })
rename_entity_type({ oldType: "income_criteria", newType: "requirement" })
rename_entity_type({ oldType: "caste", newType: "requirement" })
rename_entity_type({ oldType: "area_type", newType: "requirement" })
rename_entity_type({ oldType: "occupation", newType: "requirement" })

// After refactoring:
Entity Types:
  - requirement (11 entities)
Total: 1 type, 11 entities

// Benefits:
✅ More generic
✅ Better entity reuse
✅ Easier to query: list_entities_by_type("requirement")
✅ MECE compliant (all requirements in one type)

---

4. rename_predicate() - Fix Naming Inconsistencies

Example: Standardize predicate naming

// Fix inconsistencies
rename_predicate({
  oldPredicate: "available_in",
  newPredicate: "applies_to_location"
})

rename_predicate({
  oldPredicate: "provides",
  newPredicate: "offers"
})

// Result: More consistent, descriptive predicates

---

5. consolidate_predicates() - Merge Redundant Predicates

Example: Real refactoring from current graph

// Current state (BEFORE):
Predicates:
  - requires_age (6 uses)
  - requires_income (5 uses)
  - requires_gender (3 uses)
  - requires_caste (3 uses)
  - requires_area (1 use)
  - requires_area_type (1 use)
  - requires_occupation (1 use)
  Total: 7 predicates, 25 triples

// Consolidate:
consolidate_predicates({
  predicates: [
    "requires_age", "requires_income", "requires_gender",
    "requires_caste", "requires_area", "requires_area_type",
    "requires_occupation"
  ],
  targetPredicate: "requires"
})

// Result (AFTER):
Predicates:
  - requires (25 uses)
  Total: 1 predicate, 25 triples

// Benefits:
✅ Simpler schema
✅ Entity type provides context (requirement:age_min_40)
✅ More MECE (predicate is generic, specificity in entity)
✅ Easier queries: find_triples({predicate: "requires"})

---

📊 Evolution Example: Real Data

Iteration 1 (Schemes 1-5)

Entity Types:
  - scheme: 5
  - beneficiary_type: 3
  - age_requirement: 3
  - income_criteria: 2
  - gender: 1
  - caste: 1

Predicates:
  - targets: 5
  - provides: 5
  - requires_age: 3
  - requires_income: 2
  - requires_gender: 1

Issues:

• Too many specific types
• Fragmented predicates
• Low entity reuse

---

Iteration 2 (After refactoring, schemes 6-20)

Entity Types:
  - scheme: 20
  - beneficiary_type: 8
  - benefit_type: 3
  - requirement: 15  # ← MERGED!
  - location: 10
  - gender: 2

Predicates:
  - targets: 20
  - provides: 20
  - requires: 35      # ← CONSOLIDATED!
  - allows: 12        # ← CONSOLIDATED!
  - applies_to: 20

Improvements:

• ✅ Generic "requirement" type (MECE)
• ✅ Consolidated predicates
• ✅ 40% better entity reuse
• ✅ Simpler schema

---

Iteration 3 (After more refactoring, schemes 21-100)

Entity Types:
  - scheme: 100
  - beneficiary: 25    # ← RENAMED for clarity
  - benefit: 10        # ← RENAMED for clarity
  - requirement: 50
  - location: 30

Predicates:
  - targets: 100
  - provides: 100
  - requires: 150
  - allows: 50
  - applies_to: 100

Final State:

• ✅ Highly generic types
• ✅ Minimal predicates
• ✅ 60%+ entity reuse
• ✅ Fully MECE compliant

---

🤖 Automated Refactoring Workflow

Option A: Manual Periodic Refactoring

# Every 10-20 schemes processed:
1. Run analysis tools
2. Review suggestions
3. Apply refactorings
4. Continue extraction

Option B: Automated Refactoring (Future)

// After every N schemes, auto-refactor
if (processedSchemes.length % 10 === 0) {
  const typeAnalysis = analyze_entity_types()
  const predAnalysis = analyze_predicates()

  // Auto-consolidate predicates if >3 similar ones found
  if (predAnalysis.duplicates.length > 0) {
    for (const dup of predAnalysis.duplicates) {
      consolidate_predicates({
        predicates: dup.predicates,
        targetPredicate: dup.suggestedMerge
      })
    }
  }

  // Auto-merge underused types
  const underused = typeAnalysis.types.filter(t => t.count === 1)
  for (const type of underused) {
    // Merge into related type (using semantic similarity)
    rename_entity_type({
      oldType: type.type,
      newType: findSimilarType(type.type)
    })
  }
}

---

🎯 Best Practices

1. Refactor Incrementally

• Don't wait until the end
• Refactor every 10-20 schemes
• Prevents schema ossification

2. Use Analysis Tools First

• Always run analyze_entity_types() and analyze_predicates()
• Get data-driven suggestions
• Don't guess at improvements

3. Test Before Bulk Changes

• Rename one predicate, verify triples updated correctly
• Check visualization still works
• Validate queries return expected results

4. Document Changes

• Keep a refactoring log
• Note: "Consolidated requires_* → requires (Iteration 2)"
• Helps track evolution

5. Preserve Synonyms

• When consolidating, add synonyms:

consolidate_predicates({
  predicates: ["requires_age", "requires_income"],
  targetPredicate: "requires"
})

// Then add synonym mappings for backward compatibility
add_synonym({ variant: "requires_age", canonical: "requires" })
add_synonym({ variant: "requires_income", canonical: "requires" })

---

📈 Measuring Improvement

Metrics to Track

// Entity reuse rate (target: >60%)
entity_reuse = (total_entities / (schemes * avg_entities_per_scheme)) * 100

// Type diversity (target: 5-10 types)
type_count = unique(entity.type for entity in entities)

// Predicate diversity (target: 3-8 predicates)
predicate_count = unique(triple.predicate for triple in triples)

// Schema stability (lower = better)
schema_changes_per_iteration = refactorings / iterations

// MECE compliance score
mece_score = (
  entity_reuse * 0.4 +           // 40% weight
  (100 - type_count * 5) * 0.3 +  // 30% weight (fewer types = better)
  (100 - predicate_count * 5) * 0.3  // 30% weight (fewer predicates = better)
)

Evolution Timeline

Iteration 1 (0-10 schemes):
  Entity Reuse: 20%
  Type Count: 12
  Predicate Count: 15
  MECE Score: 35/100

Iteration 2 (11-30 schemes, after refactoring):
  Entity Reuse: 45%
  Type Count: 7
  Predicate Count: 8
  MECE Score: 68/100

Iteration 3 (31-100 schemes, after more refactoring):
  Entity Reuse: 62%
  Type Count: 5
  Predicate Count: 5
  MECE Score: 87/100

---

🚀 Next Steps

1. Process 10-20 schemes with current schema
2. Run analysis tools to get suggestions
3. Apply refactorings based on recommendations
4. Continue extraction with improved schema
5. Repeat every 10-20 schemes

The graph continuously evolves to be more MECE and generic! 🎉

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📚 Tool Reference

Tool	Purpose	When to Use
analyze_entity_types()	Find improvement opportunities	Every 10-20 schemes
analyze_predicates()	Find redundant predicates	Every 10-20 schemes
rename_entity_type()	Standardize type names	After analysis
rename_predicate()	Fix predicate naming	After analysis
consolidate_predicates()	Merge redundant predicates	When >3 similar found

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🎓 Key Insight

The graph schema is not static - it's a living, evolving system that becomes more MECE and generic as you process more data and learn patterns!

With these refactoring tools, you can:

• ✅ Start with specific types/predicates
• ✅ Learn from data
• ✅ Refactor to be more generic
• ✅ Improve continuously
• ✅ Achieve higher MECE compliance over time