Claude : SPARQL Query Management System Implementation

Project Overview

Successfully implemented a comprehensive SPARQL query management system for the Semem library to centralize, organize, and optimize SPARQL query handling across all example workflows.

Implemented Components

1. Directory Structure

Created organized structure under /sparql/:

sparql/
├── queries/
│   ├── retrieval/          # Data retrieval queries
│   ├── management/         # Graph management operations  
│   ├── search/             # Semantic search queries
│   └── visualization/      # Knowledge graph visualization
├── templates/
│   ├── prefixes.sparql     # Common namespace prefixes
│   └── fragments/          # Reusable query fragments
└── config/
    └── query-mappings.json # Query name to file mappings

2. Core Service Classes

SPARQLQueryService (src/services/sparql/SPARQLQueryService.js)

• Query loading with template parameter substitution
• Automatic prefix management
• Helper methods for common formatting tasks
• Integration with caching layer

QueryCache (src/services/sparql/QueryCache.js)

• File modification detection and cache invalidation
• LRU eviction policy with configurable size limits
• TTL-based expiration
• Performance monitoring and statistics

3. Query Extraction and Organization

Extracted 16+ hardcoded queries from examples and organized them:

Retrieval Queries:

• questions-with-relationships.sparql - Question navigation relationships
• entity-content-retrieval.sparql - Entity content fetching
• navigation-questions.sparql - ZPT navigation with embeddings
• corpus-loading.sparql - Corpus data with embeddings and concepts
• processed-questions.sparql - Document QA question processing
• document-chunks.sparql - Document chunk retrieval with metadata
• enhanced-questions-wikidata.sparql - Multi-source question enhancement

Management Queries:

• insert-data.sparql - Basic INSERT DATA operations
• clear-graph.sparql - Graph clearing operations
• relationship-creation.sparql - Relationship entity creation
• context-results-storage.sparql - Context retrieval result storage

Search Queries:

• ppr-concepts.sparql - PPR seed concept extraction
• importance-rankings.sparql - Corpuscle importance scoring
• ppr-results-export.sparql - PPR search result storage
• document-chunks-count.sparql - Document chunk counting

Visualization Queries:

• knowledge-graph-construct.sparql - Multi-domain knowledge graph visualization

4. Template System

Common Prefixes: Standardized namespace declarations across all queries

PREFIX ragno: <http://purl.org/stuff/ragno/>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX dcterms: <http://purl.org/dc/terms/>
PREFIX prov: <http://www.w3.org/ns/prov#>
PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
PREFIX owl: <http://www.w3.org/2002/07/owl#>
PREFIX semem: <http://semem.hyperdata.it/>

Query Fragments: Reusable patterns for common operations

• embedding-attributes.sparql - Backward-compatible embedding patterns
• concept-attributes.sparql - Concept attribute extraction
• flow-stage-filter.sparql - Processing stage filtering

5. Example Workflow Updates

Created updated versions demonstrating integration:

• GetResult-updated.js - BeerQA result generation with query service
• 03-retrieve-context-updated.js - Document QA context retrieval

Key Features

Performance Optimizations

• Caching Layer: File-based invalidation with LRU eviction
• Parallel Loading: Async query and prefix loading
• Template Reuse: Minimize parsing overhead through caching

Developer Experience

• Centralized Management: All queries in organized file structure
• Parameter Substitution: Clean template system with ${parameter} syntax
• Helper Methods: Common formatting operations (entity lists, timestamps, etc.)
• Error Handling: Informative error messages with file paths

Maintainability

• Separation of Concerns: Queries separated from application logic
• Consistent Patterns: Standardized prefixes and query structure
• Version Control Friendly: Individual files for easy diff tracking

Testing Results

Comprehensive test suite validates:

• ✅ Service initialization and configuration
• ✅ Query loading and caching (16 available queries)
• ✅ Template parameter substitution
• ✅ Cache performance (1ms cached retrieval)
• ✅ Helper method functionality
• ✅ File modification detection
• ⚠ SPARQL endpoint integration (config-dependent)

Integration Benefits

Before

• Hardcoded queries scattered across 15+ files
• Duplicated prefix declarations
• No caching or optimization
• Difficult maintenance and debugging

After

• Centralized query repository with organized categories
• Automatic caching with file-based invalidation
• Consistent template system with parameter substitution
• Easy integration: queryService.getQuery('query-name', params)

Usage Pattern

import { getDefaultQueryService } from '../../src/services/sparql/index.js';

const queryService = getDefaultQueryService();
const query = await queryService.getQuery('questions-with-relationships', {
    graphURI: 'http://example.org/graph'
});
const result = await sparqlHelper.executeSelect(query);

Next Steps

1. Migration: Update remaining example workflows to use query service
2. Extension: Add query validation and SPARQL syntax checking
3. Monitoring: Query performance metrics and usage analytics
4. Documentation: API documentation and usage examples

The implemented system provides a solid foundation for scalable SPARQL query management while maintaining backward compatibility with existing SPARQLHelper infrastructure.