• Introduction
  • Vision
  • Mission
• Guides & Tutorials
  • 1. Our Indexes
    • 1.1. Wikidata+GeoNames Index (Augmentation)
    • 1.2. WHG Publication Index (Searchable Publication)
    • 1.3. WHG Union Index (Final Integration & Clustering)
  • 2. Workbench
    • 2.1. Individual datasets
    • 2.2. Multiple datasets
    • 2.3. Thematic place collections
      • 2.3.1. Instructional exercise in a class setting, or workshop
      • 2.3.2. Authored publication
  • 3. Publishing Data
    • 3.1. Create and publish a Place Collection
    • 3.2. Create and publish a Dataset Collection
  • 4. Uploading Data
    • 4.1. Choosing an upload data format: LPF or LP-TSV?
    • 4.2. Preparing data for upload
      • 4.2.1. The simple case
      • 4.2.2. The not so simple case: extracting places
  • 5. Reconciliation & Accessioning
    • 5.1. What does closeMatch mean?
  • 6. Reviewing accessioning results
  • 7. Collection Groups
    • 7.1. Create and manage a Collection Group for a class or workshop
• Technical
  • 1. Repositories
  • 2. APIs
    • 2.1. Entity API
    • 2.2. Reconciliation Service API
      • 2.2.1. Using the WHG Reconciliation API in OpenRefine
    • 2.3. API Tokens
      • 2.3.1. Using an API Token
  • 3. Issues
• Development Roadmap
  • v3.5: Toponym Phonetics
    • 1. Overview
      • 1.1. Goals
      • 1.2. Why Not Elasticsearch’s Built-in Phonetic Analysis?
      • 1.3. Limitations
      • 1.4. Architecture Summary
      • 1.5. Data Sources
    • 2. Elastic Management Guide
      • 2.1. Table of Contents
      • 2.2. Architecture Overview
      • 2.3. Installation
      • 2.4. Configuration
      • 2.5. Storage Architecture
      • 2.6. Production Instance (VM)
      • 2.7. Staging Instance (Slurm)
      • 2.8. Authority Data Ingestion
      • 2.9. Index Management
      • 2.10. Snapshot Management
      • 2.11. Production Deployment
      • 2.12. Health Monitoring
      • 2.13. Troubleshooting
      • 2.14. Quick Reference
    • 3. Infrastructure Summary
      • 3.1. Overview
      • 3.2. Architecture
      • 3.3. Authority Data Sources
      • 3.4. WHG-Contributed Datasets
      • 3.5. Phonetic Search
      • 3.6. Index Schemas
      • 3.7. Deployment Strategy
      • 3.8. Storage Requirements
      • 3.9. Snapshot Strategy
      • 3.10. Resource Summary
      • 3.11. Operational Commands
      • 3.12. Directory Structure
      • 3.13. References
    • 4. Components
      • 4.1. Infrastructure
      • 4.2. Elasticsearch Indices
      • 4.3. Processing Components
    • 5. Data Flow
      • 5.1. Authority File Ingestion
      • 5.2. WHG-Contributed Dataset Ingestion
      • 5.3. Embedding Generation
      • 5.4. Incremental Updates
    • 6. Elasticsearch Index Design
      • 6.1. Index Schemas
      • 6.2. Ingest Pipelines
      • 6.3. HNSW Configuration
      • 6.4. Analysers
    • 7. Training the Phonetic Similarity Model
      • 7.1. 1. Architecture Overview
      • 7.2. 2. Key Features
      • 7.3. 3. Installation
      • 7.4. 4. The Training Pipeline
      • 7.5. 5. Python Inference API
      • 7.6. 6. Configuration
    • 8. Query Pipeline
      • 8.1. Search Strategy
      • 8.2. Elasticsearch Query Structure
      • 8.3. Error Handling
      • 8.4. Performance Optimisation
    • 9. Advantages of This Architecture
      • 9.1. Two-Instance Isolation
      • 9.2. Unified Infrastructure
      • 9.3. Scalable Storage Tiers
      • 9.4. Zero-Downtime Deployments
      • 9.5. Flexible Embedding Generation
      • 9.6. Toponym Deduplication
      • 9.7. Efficient Phonetic Search
      • 9.8. Graceful Degradation
      • 9.9. Unified Search Across Sources
      • 9.10. Maintainability
      • 9.11. Research Reproducibility
    • 10. Monitoring & Observability
      • 10.1. Key Metrics
      • 10.2. Health Check Endpoints
      • 10.3. Log Locations
      • 10.4. Dashboards
      • 10.5. Alerting
      • 10.6. Runbook: Common Issues
    • 11. Future Extensions
      • 11.1. Short-Term Enhancements
      • 11.2. Medium-Term Development
      • 11.3. Long-Term Vision
    • 12. Deployment Plan
      • 12.1. Phase 1: Infrastructure Setup
      • 12.2. Phase 2: Core Index Population
      • 12.3. Phase 3: Model Training
      • 12.4. Phase 4: Embedding Generation
      • 12.5. Phase 5: Query Integration
      • 12.6. Phase 6: Production Rollout
      • 12.7. Ongoing Operations
    • 13. Risk Assessment
      • 13.1. Technical Risks
      • 13.2. Operational Risks
      • 13.3. Data Quality Risks
      • 13.4. Mitigation Strategies
    • 14. Success Criteria
      • 14.1. Technical Metrics
      • 14.2. User Experience Metrics
      • 14.3. Research Impact
      • 14.4. Acceptance Criteria by Phase
    • 15. Summary & References
      • 15.1. Key Design Decisions
      • 15.2. Technology Stack
      • 15.3. Storage Requirements
      • 15.4. Timeline
      • 15.5. References
  • V4: Graph Datamodel
    • 1. User Guide
      • 1.1. Note to Documentation Team
      • 1.2. Getting Help
    • 2. Open Educational Resources (OER)
      • 2.1. Vision
      • 2.2. Strategic Goals
      • 2.3. Technical Requirements
    • 3. Data Model
      • 3.1. Introduction
      • 3.2. Overview
      • 3.3. Attestations & Relations
      • 3.4. Vocabularies
      • 3.5. Special Thing Patterns
      • 3.6. Contribution Types & Data Formats
      • 3.7. RDF Representation
      • 3.8. Platform Use Cases
      • 3.9. Implementation in ArangoDB
      • 3.10. Summary & Future Directions
    • 4. System Architecture
      • 4.1. Database Technology Assessment
      • 4.2. Kubernetes Configuration
      • 4.3. SSH Key Setup
      • 4.4. Deploying the Management Pod
      • 4.5. Deploying Services
      • 4.6. Service Configuration
• License
  • Creative Commons Attribution-NonCommercial 4.0 International Public License
    • Section 1 – Definitions.
    • Section 2 – Scope.
    • Section 3 – License Conditions.
    • Section 4 – Sui Generis Database Rights.
    • Section 5 – Disclaimer of Warranties and Limitation of Liability.
    • Section 6 – Term and Termination.
    • Section 7 – Other Terms and Conditions.
    • Section 8 – Interpretation.