semantica/docs/learning-more.md

Learning More

Additional resources, tutorials, and advanced learning materials for Semantica.

Additional Tutorials

Video Tutorials

Coming soon! We're working on video tutorials covering:

• Getting started with Semantica
• Building your first knowledge graph
• Advanced techniques and patterns
• Real-world use cases

Blog Posts & Articles

Stay tuned for blog posts covering:

• Best practices for knowledge graph construction
• Performance optimization tips
• Integration guides
• Case studies and success stories

Best Practices

Knowledge Graph Design

1. Start with Clear Objectives

   • Define what you want to extract
   • Identify key entities and relationships
   • Plan your schema before processing

2. Iterate and Refine

   • Start with a small dataset
   • Validate extracted entities
   • Refine extraction patterns
   • Scale up gradually

3. Quality Over Quantity

   • Focus on accuracy
   • Validate relationships
   • Resolve conflicts early
   • Maintain data quality

Performance Tips

# Process in batches for large datasets
sources = ["doc1.pdf", "doc2.pdf", "doc3.pdf"]
batch_size = 10

for i in range(0, len(sources), batch_size):
    batch = sources[i:i+batch_size]
    result = semantica.build_knowledge_base(batch)
    # Process and save results

Integration Patterns

Pattern 1: Incremental Building

# Build knowledge graph incrementally
kg = None
for source in sources:
    result = semantica.build_knowledge_base([source])
    if kg is None:
        kg = result["knowledge_graph"]
    else:
        kg = semantica.kg.merge([kg, result["knowledge_graph"]])

Pattern 2: Pipeline Processing

# Create a processing pipeline
pipeline = [
    ("ingest", semantica.ingest.from_file),
    ("parse", semantica.parse.document),
    ("extract", semantica.semantic_extract.entities),
    ("build", semantica.kg.build_graph)
]

for step_name, step_func in pipeline:
    data = step_func(data)

Advanced Topics

Custom Extractors

Create custom entity extractors:

from semantica.semantic_extract import BaseExtractor

class CustomExtractor(BaseExtractor):
    def extract(self, text):
        # Your custom extraction logic
        return entities

Custom Export Formats

Add custom export formats:

from semantica.export import BaseExporter

class CustomExporter(BaseExporter):
    def export(self, kg, path):
        # Your custom export logic
        pass

Performance Optimization

• Use GPU acceleration when available
• Process documents in parallel
• Cache embeddings
• Optimize graph queries

Community Resources

GitHub Discussions

Join discussions on:

• General Discussion
• Q&A
• Show and Tell

Contributing

Want to contribute? See our Contributing Guide.

Examples Repository

Check out the examples repository for more code samples.

Related Projects

GraphRAG

Semantica works great with GraphRAG implementations. See our GraphRAG examples.

Vector Databases

Integrate with vector databases:

• Pinecone
• Weaviate
• Qdrant
• Milvus

Knowledge Graph Databases

Export to and work with:

• Neo4j
• Amazon Neptune
• ArangoDB
• Blazegraph

Next Steps

• Deep Dive - Advanced architecture and internals
• API Reference - Complete API documentation
• Cookbook - Interactive tutorials
• Examples - More code examples

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Have questions or suggestions? Open an issue or start a discussion!