semantica/docs/quickstart.md

Quickstart

Get started with Semantica in 5 minutes. This guide will walk you through building your first knowledge graph.

!!! tip "Before You Start" Make sure you have Semantica installed. If not, follow the Installation Guide first. This quickstart assumes basic Python knowledge.

Overview

flowchart LR
    A[Install] --> B[Initialize]
    B --> C[Load Data]
    C --> D[Extract]
    D --> E[Build Graph]
    E --> F[Visualize]
    
    style A fill:#e3f2fd
    style F fill:#c8e6c9

Step 1: Installation

If you haven't installed Semantica yet:

pip install semantica

See the Installation Guide for detailed instructions.

!!! note "Installation Options" For production use, consider installing with optional dependencies for better performance: pip install semantica[all]. See the Installation Guide for all options.

Step 2: Your First Knowledge Graph

Let's build a knowledge graph from a document:

from semantica import Semantica

# Initialize Semantica
semantica = Semantica()

# Build knowledge graph from a document
result = semantica.build_knowledge_base(
    sources=["document.pdf"],
    embeddings=True,
    graph=True
)

# Access results
kg = result["knowledge_graph"]
embeddings = result["embeddings"]
statistics = result["statistics"]

print(f"Extracted {len(kg['entities'])} entities")
print(f"Created {len(kg['relationships'])} relationships")
print(f"Generated {len(embeddings)} embeddings")

Expected Output:

Extracted 45 entities
Created 32 relationships
Generated 45 embeddings

Step 3: Extract Entities and Relationships

Extract structured information from text:

from semantica.semantic_extract import NamedEntityRecognizer, RelationExtractor

# Sample text
text = """
Apple Inc. was founded by Steve Jobs in Cupertino, California in 1976.
The company designs and manufactures consumer electronics and software.
Tim Cook is the current CEO of Apple.
"""

# Extract entities
ner = NamedEntityRecognizer()
entities = ner.extract_entities(text)

print("Extracted Entities:")
for entity in entities:
    print(f"  - {entity.text} ({entity.label})")

# Extract relationships
rel_extractor = RelationExtractor()
relationships = rel_extractor.extract_relations(text, entities=entities)

print("\nExtracted Relationships:")
for rel in relationships:
    print(f"  - {rel.subject.text} --[{rel.predicate}]--> {rel.object.text}")

Expected Output:

Extracted Entities:
  - Apple Inc. (ORGANIZATION)
  - Steve Jobs (PERSON)
  - Cupertino (LOCATION)
  - California (LOCATION)
  - Tim Cook (PERSON)

Extracted Relationships:
  - Apple Inc. --[founded_by]--> Steve Jobs
  - Apple Inc. --[located_in]--> Cupertino
  - Apple Inc. --[has_ceo]--> Tim Cook

Step 4: Build Knowledge Graph from Multiple Sources

Combine data from multiple sources:

from semantica import Semantica

semantica = Semantica()

# Multiple data sources
sources = [
    "documents/research_paper.pdf",
    "documents/company_report.docx",
    "https://example.com/news-article"
]

# Build unified knowledge graph
result = semantica.build_knowledge_base(
    sources=sources,
    embeddings=True,
    graph=True,
    normalize=True
)

kg = result["knowledge_graph"]

# Analyze the graph
print(f"Total entities: {len(kg['entities'])}")
print(f"Total relationships: {len(kg['relationships'])}")
print(f"Sources processed: {len(result['metadata']['sources'])}")

Step 5: Visualize Your Knowledge Graph

Visualize the knowledge graph you created:

from semantica import Semantica
from semantica.visualization import KGVisualizer

semantica = Semantica()

# Build graph
result = semantica.build_knowledge_base(["document.pdf"])
kg = result["knowledge_graph"]

# Visualize
visualizer = KGVisualizer()
visualizer.visualize_network(kg, output="html", file_path="graph.html")
print("Graph visualization saved to graph.html")

Open graph.html in your browser to see an interactive visualization.

Step 6: Export Your Knowledge Graph

Export your knowledge graph in various formats:

from semantica import Semantica
from semantica.export import export_rdf, export_json, export_csv, export_owl

semantica = Semantica()

# Build graph
result = semantica.build_knowledge_base(["data.pdf"])
kg = result["knowledge_graph"]

# Export to different formats
export_rdf(kg, "output.rdf")      # RDF/XML format
export_json(kg, "output.json")    # JSON format
export_csv(kg, "output.csv")      # CSV format
export_owl(kg, "output.owl")       # OWL ontology format

print("Exported knowledge graph to multiple formats")

Common Patterns

Pattern 1: Process Text Directly

from semantica import Semantica

semantica = Semantica()

text = "Your text content here..."
result = semantica.process_document(text)

Pattern 2: Custom Configuration

from semantica import Semantica, Config

# Create custom configuration
config = Config(
    embeddings=True,
    graph=True,
    normalize=True,
    conflict_resolution="voting"
)

semantica = Semantica(config=config)
result = semantica.build_knowledge_base(["document.pdf"])

Pattern 3: Incremental Building

from semantica import Semantica

semantica = Semantica()

# Build incrementally
kg1 = semantica.kg.build_graph(["source1.pdf"])
kg2 = semantica.kg.build_graph(["source2.pdf"])

# Merge knowledge graphs
merged_kg = semantica.kg.merge([kg1, kg2])

Next Steps

Now that you've built your first knowledge graph:

1. Explore Examples - See more advanced use cases
2. **API Reference - Learn about all available methods
3. Cookbook - Interactive Jupyter notebooks
4. Full Documentation - Comprehensive guide

Troubleshooting

Common Issues

Issue: No entities extracted

• Solution: Check that your document contains text content. PDFs with images only won't work without OCR.

Issue: Slow processing

• Solution: For large documents, consider processing in chunks or using GPU acceleration.

Issue: Memory errors

• Solution: Process documents one at a time or reduce batch sizes.

Need help? Check the Installation Troubleshooting or GitHub Issues.