# Examples Real-world examples and use cases for Semantica. !!! tip "Interactive Learning" For hands-on interactive tutorials, check out our [Cookbook](cookbook.md) with Jupyter notebooks covering everything from basics to advanced use cases. ## Basic Examples !!! note "Code Examples" All examples assume you have Semantica installed and imported. See the [Installation Guide](installation.md) if you need to set it up first. ### Example 1: Basic Knowledge Graph Build a knowledge graph from a single document: ```python from semantica import Semantica semantica = Semantica() # Build KG from PDF result = semantica.build_knowledge_base( sources=["research_paper.pdf"], embeddings=True, graph=True ) kg = result["knowledge_graph"] print(f"Entities: {len(kg['entities'])}") print(f"Relationships: {len(kg['relationships'])}") ``` ### Example 2: Entity Extraction Extract entities from text: ```python from semantica import Semantica semantica = Semantica() text = """ Apple Inc. is a technology company founded by Steve Jobs. The company is headquartered in Cupertino, California. Tim Cook is the current CEO of Apple. """ entities = semantica.semantic_extract.extract_entities(text) for entity in entities["entities"]: print(f"{entity['text']}: {entity['type']}") ``` **Output:** ``` Apple Inc.: ORGANIZATION Steve Jobs: PERSON Cupertino: LOCATION California: LOCATION Tim Cook: PERSON ``` ### Example 3: Multi-Source Integration Combine data from multiple sources: ```python from semantica import Semantica semantica = Semantica() sources = [ "documents/finance_report.pdf", "documents/market_analysis.docx", "https://example.com/news-article" ] result = semantica.build_knowledge_base(sources) kg = result["knowledge_graph"] print(f"Unified knowledge graph with {len(kg['entities'])} entities") ``` ### Example 4: Export Formats Export knowledge graph to multiple formats: ```python from semantica import Semantica semantica = Semantica() kg = semantica.kg.build_graph(["data.pdf"]) # Export to different formats semantica.export.to_rdf(kg, "output.rdf") semantica.export.to_json(kg, "output.json") semantica.export.to_csv(kg, "output.csv") semantica.export.to_owl(kg, "output.owl") ``` ## Advanced Examples ### Example 5: Conflict Resolution Resolve conflicts in data from multiple sources: ```python from semantica import Semantica from semantica.conflicts import ConflictResolver semantica = Semantica() # Build graph from multiple sources result = semantica.build_knowledge_base([ "source1.pdf", "source2.pdf", "source3.pdf" ]) # Detect conflicts conflicts = semantica.kg.detect_conflicts(result["knowledge_graph"]) # Resolve conflicts resolver = ConflictResolver(default_strategy="voting") resolved = resolver.resolve_conflicts(conflicts) print(f"Resolved {len(resolved)} conflicts") ``` ### Example 6: Custom Configuration Use custom configuration for specific use cases: ```python from semantica import Semantica, Config # Custom configuration config = Config( embeddings=True, graph=True, normalize=True, conflict_resolution="highest_confidence" ) semantica = Semantica(config=config) result = semantica.build_knowledge_base(["document.pdf"]) ``` ### Example 7: Incremental Graph Building Build knowledge graph incrementally: ```python from semantica import Semantica semantica = Semantica() # Build graphs separately kg1 = semantica.kg.build_graph(["source1.pdf"]) kg2 = semantica.kg.build_graph(["source2.pdf"]) kg3 = semantica.kg.build_graph(["source3.pdf"]) # Merge into unified graph merged_kg = semantica.kg.merge([kg1, kg2, kg3]) print(f"Merged graph: {len(merged_kg['entities'])} entities") ``` ### Example 8: Visualization Create interactive visualizations: ```python from semantica import Semantica semantica = Semantica() # Build graph result = semantica.build_knowledge_base(["document.pdf"]) kg = result["knowledge_graph"] # Visualize semantica.kg.visualize(kg, output_path="graph.html") # Also analyze analysis = semantica.kg.analyze(kg) print(f"Graph density: {analysis['density']}") print(f"Connected components: {analysis['components']}") ``` ## Use Case Examples ### Research Paper Analysis Extract knowledge from research papers: ```python from semantica import Semantica semantica = Semantica() # Process research paper result = semantica.build_knowledge_base([ "papers/ai_research.pdf", "papers/ml_survey.pdf" ]) kg = result["knowledge_graph"] # Find key concepts concepts = [e for e in kg["entities"] if e["type"] == "CONCEPT"] print(f"Found {len(concepts)} key concepts") ``` ### Company Intelligence Build knowledge graph from company documents: ```python from semantica import Semantica semantica = Semantica() # Company documents sources = [ "company/annual_report.pdf", "company/press_releases/", "company/website_content.html" ] result = semantica.build_knowledge_base(sources) kg = result["knowledge_graph"] # Export for analysis semantica.export.to_json(kg, "company_intelligence.json") ``` ### News Article Processing Process and analyze news articles: ```python from semantica import Semantica semantica = Semantica() # News articles articles = [ "https://example.com/article1", "https://example.com/article2", "https://example.com/article3" ] result = semantica.build_knowledge_base(articles) kg = result["knowledge_graph"] # Extract key entities people = [e for e in kg["entities"] if e["type"] == "PERSON"] organizations = [e for e in kg["entities"] if e["type"] == "ORGANIZATION"] print(f"People mentioned: {len(people)}") print(f"Organizations: {len(organizations)}") ``` ## Interactive Examples For more interactive examples and tutorials, check out our [Cookbook](cookbook.md) with Jupyter notebooks covering: - **Introduction**: Getting started tutorials - **Advanced**: Advanced techniques and patterns - **Use Cases**: Real-world applications in various domains ## More Resources - **[Quick Start Guide](quickstart.md)** - Step-by-step tutorial - **[API Reference](api.md)** - Complete API documentation - **[Cookbook](cookbook.md)** - Interactive Jupyter notebooks - **[Code Examples](../CodeExamples.md)** - Additional code samples