name: Knowledge Graph Builder
slug: knowledge-graph-builder
description: Build knowledge graphs for support systems, connecting concepts, articles, and solutions
category: customer-support
complexity: complex
version: "1.0.0"
author: "ID8Labs"
triggers:
- "knowledge graph"
- "knowledge mapping"
- "concept relationships"
- "support knowledge base"
- "semantic connections"
- "knowledge network"
tags:
- knowledge-graph
- knowledge-management
- semantic-search
- support-systems
- content-architecture

Knowledge Graph Builder

Expert knowledge graph creation system that transforms disconnected support content into an interconnected web of concepts, relationships, and solutions. This skill provides structured workflows for mapping knowledge domains, defining relationships, and powering intelligent support experiences.

Knowledge graphs enable support systems to understand context, not just keywords. When a customer asks about "billing issues," a knowledge graph knows this relates to invoices, payment methods, subscription plans, and potentially churn risk. This skill helps you build that connective intelligence.

Built on semantic web principles and knowledge engineering best practices, this skill combines domain modeling, relationship mapping, and practical implementation to create knowledge graphs that power smarter support.

Core Workflows

Workflow 1: Domain Modeling

Define the concepts and entities in your knowledge domain

1. Entity Identification
2. Core Entities: Products, features, concepts
3. Customer Entities: Accounts, users, segments
4. Support Entities: Issues, solutions, articles
5. Process Entities: Workflows, procedures, steps

6. Context Entities: Use cases, personas, scenarios

7. Entity Types for Support
   | Entity Type | Examples | Purpose |
   |-------------|----------|---------|
   | Product | App, Feature, Module | What customers use |
   | Issue | Bug, Error, Question | What customers face |
   | Solution | Fix, Workaround, Guide | How to resolve |
   | Article | FAQ, How-to, Reference | Content resources |
   | Concept | Term, Process, Capability | Understanding |
   | Persona | Admin, User, Developer | Who needs help |

8. Entity Properties
   ```
   Entity: Feature
   Properties:

9. id: unique identifier
10. name: display name
11. description: what it does
12. status: active/deprecated/beta
13. complexity: basic/intermediate/advanced
14. related_persona: who uses it

15. documentation_url: help article link
    ```

16. Entity Extraction Sources

17. Product documentation
18. Support ticket taxonomy
19. FAQ categories
20. Help center structure
21. Feature specifications
22. User research findings

Workflow 2: Relationship Mapping

Define how entities connect to each other

1. Core Relationship Types
   | Relationship | From | To | Example |
   |--------------|------|-----|---------|
   | SOLVES | Solution | Issue | "Password reset SOLVES login failure" |
   | PART_OF | Feature | Product | "Dashboard PART_OF Analytics" |
   | REQUIRES | Feature | Feature | "Export REQUIRES Pro plan" |
   | CAUSES | Issue | Issue | "API limit CAUSES sync failure" |
   | DOCUMENTED_IN | Concept | Article | "Billing DOCUMENTED_IN pricing guide" |
   | APPLIES_TO | Solution | Persona | "Workaround APPLIES_TO admin users" |

2. Relationship Properties
   ```
   Relationship: SOLVES
   Properties:

3. confidence: how reliable (0-1)
4. conditions: when this applies
5. effectiveness: success rate

6. last_verified: date checked
   ```

7. Relationship Discovery

8. Analyze support ticket resolution paths
9. Map help article cross-references
10. Identify co-occurring issues
11. Study user journey patterns

12. Review expert knowledge

13. Relationship Strength

14. Strong: Always true, well documented
15. Moderate: Usually true, common pattern
16. Weak: Sometimes true, contextual
17. Suggested: Possible, needs validation

Workflow 3: Graph Construction

Build the actual knowledge graph structure

1. Graph Architecture
   ```
   Nodes (Entities):
2. Unique identifier
3. Entity type
4. Properties
5. Metadata (created, updated, source)

Edges (Relationships):
- From node
- To node
- Relationship type
- Properties
- Metadata
```

1. Implementation Options
   | Approach | Best For | Tools |
   |----------|----------|-------|
   | Graph Database | Complex queries, scale | Neo4j, Amazon Neptune |
   | RDF Triple Store | Semantic web, standards | Apache Jena, Stardog |
   | Property Graph | Flexible modeling | Neo4j, TigerGraph |
   | Embedded | Simple use cases | NetworkX, GraphQL |

2. Schema Design

3. Define node labels/types
4. Define relationship types
5. Set required properties
6. Define constraints (uniqueness, existence)

7. Create indexes for query performance

8. Data Population

9. Bulk import from existing sources
10. API ingestion from live systems
11. Manual expert curation
12. Automated extraction (NLP)
13. Continuous updates from support activity

Workflow 4: Query & Inference

Extract value from the knowledge graph

1. Query Patterns
   | Query Type | Use Case | Example |
   |------------|----------|---------|
   | Traversal | Find related content | "Articles related to X" |
   | Path finding | Solution discovery | "Steps from issue to resolution" |
   | Pattern matching | Similar issues | "Issues like X" |
   | Aggregation | Analytics | "Most common issue per feature" |
   | Recommendation | Suggestions | "Other users also viewed" |

2. Inference Rules

3. If A CAUSES B and B CAUSES C, suggest A might relate to C
4. If Solution S SOLVES Issue I, suggest similar solutions for similar issues
5. If Article A DOCUMENTED_IN B and B REQUIRES C, show C as prerequisite

6. If many users navigate A → B → C, suggest shortcut

7. Semantic Search Enhancement

8. Expand search with related concepts
9. Rank by graph centrality
10. Surface related articles
11. Suggest alternative terms

12. Understand context from relationships

13. Conversational AI Integration

14. Map user intent to graph entities
15. Follow relationships to find answers
16. Generate responses from connected content
17. Explain reasoning through graph path

Workflow 5: Maintenance & Evolution

Keep the knowledge graph accurate and growing

1. Quality Monitoring
2. Track usage patterns
3. Identify dead-end paths
4. Find orphaned nodes
5. Monitor relationship accuracy

6. Measure search success rates

7. Update Triggers

8. New product releases
9. Documentation changes
10. Support ticket patterns
11. User feedback

12. Expert curation sessions

13. Validation Process

14. Expert review of new relationships
15. A/B test graph-powered features
16. Monitor accuracy metrics
17. User feedback collection

18. Regular audits

19. Growth Strategies

20. Automated entity extraction from tickets
21. ML-based relationship suggestion
22. User contribution mechanisms
23. Expert knowledge capture sessions
24. Cross-reference with external sources

Quick Reference

Best Practices

Domain Modeling

• Start with core product concepts
• Model from customer perspective
• Keep entity types focused
• Document entity definitions clearly
• Version your schema

Relationship Design

• Use verb-based relationship names
• Make relationships directional
• Add confidence/strength properties
• Avoid redundant relationships
• Document relationship semantics

Graph Construction

• Start small, grow iteratively
• Validate with domain experts
• Index frequently queried properties
• Plan for scale from start
• Maintain data lineage

Query Optimization

• Profile query performance
• Create appropriate indexes
• Cache common traversals
• Paginate large results
• Monitor query patterns

Maintenance

• Schedule regular audits
• Track content freshness
• Remove stale nodes/edges
• Validate automated additions
• Document changes

Knowledge Graph Schema

Core Entities

Product:
  properties:
    - id: string (required, unique)
    - name: string (required)
    - description: text
    - version: string
    - status: enum [active, deprecated, beta]
    - tier: enum [free, pro, enterprise]

Feature:
  properties:
    - id: string (required, unique)
    - name: string (required)
    - description: text
    - complexity: enum [basic, intermediate, advanced]
    - introduced_version: string
    - documentation_url: url

Issue:
  properties:
    - id: string (required, unique)
    - title: string (required)
    - description: text
    - severity: enum [critical, high, medium, low]
    - frequency: enum [common, occasional, rare]
    - symptoms: array[string]

Solution:
  properties:
    - id: string (required, unique)
    - title: string (required)
    - steps: array[string]
    - type: enum [fix, workaround, configuration]
    - effectiveness: float [0-1]
    - applies_to: array[string]

Article:
  properties:
    - id: string (required, unique)
    - title: string (required)
    - url: url (required)
    - type: enum [faq, how-to, reference, troubleshooting]
    - audience: enum [all, admin, developer]
    - last_updated: date

Concept:
  properties:
    - id: string (required, unique)
    - term: string (required)
    - definition: text (required)
    - aliases: array[string]
    - domain: string

Core Relationships

PART_OF:
  from: [Feature, Concept]
  to: [Product, Feature, Concept]
  properties:
    - required: boolean

SOLVES:
  from: Solution
  to: Issue
  properties:
    - confidence: float [0-1]
    - conditions: text
    - verified_date: date

CAUSES:
  from: Issue
  to: Issue
  properties:
    - probability: float [0-1]
    - mechanism: text

DOCUMENTED_IN:
  from: [Feature, Issue, Solution, Concept]
  to: Article
  properties:
    - section: string
    - is_primary: boolean

REQUIRES:
  from: [Feature, Solution]
  to: [Feature, Permission, Plan]
  properties:
    - type: enum [prerequisite, dependency, permission]

RELATED_TO:
  from: [any]
  to: [any]
  properties:
    - strength: float [0-1]
    - type: enum [similar, alternative, complementary]

APPLIES_TO:
  from: [Solution, Article]
  to: [Persona, Plan, Version]
  properties:
    - conditions: text

Implementation Guide

Phase 1: Foundation (Week 1-2)

1. Define core entity types
2. Document property schemas
3. Map primary relationships
4. Choose technology stack
5. Set up development environment

Phase 2: Core Graph (Week 3-4)

1. Extract entities from documentation
2. Create initial relationships
3. Import into graph database
4. Build basic query interface
5. Validate with domain experts

Phase 3: Integration (Week 5-6)

1. Connect to support system
2. Implement search enhancement
3. Add content recommendation
4. Create admin interface
5. Set up monitoring

Phase 4: Intelligence (Week 7-8)

1. Add inference rules
2. Implement similarity scoring
3. Enable automated extraction
4. Build feedback loops
5. Deploy to production

Red Flags

• Disconnected nodes: Entities without relationships
• Relationship soup: Too many weak relationships
• Stale data: Outdated information not updated
• Schema drift: Inconsistent entity modeling
• Query complexity: Simple questions need complex queries
• No validation: Automated additions not verified
• Missing context: Relationships without properties
• Poor coverage: Key concepts not represented

Success Metrics