Delete cookbook directory

cookbook/__init__.py

@@ -1,37 +0,0 @@
-"""
-Semantica Cookbook
-
-This module contains comprehensive examples and use cases for the Semantica framework.
-
-The cookbook is organized into different categories:
-    - Basic Examples: Simple usage examples
-    - Advanced Examples: Complex use cases and workflows
-    - Domain Examples: Domain-specific applications
-    - Integration Examples: Integration with other systems
-    - Performance Examples: Performance optimization examples
-"""
-
-# Basic Examples
-# from .basic_examples import document_processing_example
-# from .basic_examples import web_scraping_example
-# from .basic_examples import knowledge_graph_example
-
-# Advanced Examples
-# from .advanced_examples import multi_modal_processing
-# from .advanced_examples import real_time_analytics
-# from .advanced_examples import federated_knowledge
-
-# Domain Examples
-# from .domain_examples import biomedical_knowledge
-# from .domain_examples import financial_analytics
-# from .domain_examples import legal_document_analysis
-
-# Integration Examples
-# from .integration_examples import database_integration
-# from .integration_examples import api_integration
-# from .integration_examples import cloud_integration
-
-# Performance Examples
-# from .performance_examples import large_scale_processing
-# from .performance_examples import streaming_optimization
-# from .performance_examples import memory_optimization

cookbook/advanced_examples/__init__.py

@@ -1,18 +0,0 @@
-"""
-Advanced Examples
-
-This module contains advanced usage examples for the Semantica framework.
-
-Examples:
-    - multi_modal_processing: Process multi-modal data (text, images, audio)
-    - real_time_analytics: Real-time data processing and analytics
-    - federated_knowledge: Federated knowledge graph construction
-    - streaming_processing: Stream processing and real-time updates
-    - distributed_processing: Distributed processing across multiple nodes
-"""
-
-# from .multi_modal_processing import MultiModalProcessingExample
-# from .real_time_analytics import RealTimeAnalyticsExample
-# from .federated_knowledge import FederatedKnowledgeExample
-# from .streaming_processing import StreamingProcessingExample
-# from .distributed_processing import DistributedProcessingExample

cookbook/advanced_examples/multi_modal_processing.py

@@ -1,302 +0,0 @@
-"""
-Multi-Modal Processing Example
-
-This example demonstrates how to process multi-modal data (text, images, audio) using Semantica.
-
-Key Features Demonstrated:
-    - Multi-modal data ingestion
-    - Cross-modal embedding generation
-    - Multi-modal entity extraction
-    - Cross-modal relationship detection
-    - Multi-modal knowledge graph construction
-    - Cross-modal similarity search
-
-Use Cases:
-    - Social media content analysis
-    - Multimedia document processing
-    - Video content analysis
-    - Audio transcription and analysis
-    - Cross-modal information retrieval
-"""
-
-
-class MultiModalProcessingExample:
-    """
-    Multi-modal processing example implementation.
-    
-    This example shows how to:
-    • Process data from multiple modalities
-    • Generate cross-modal embeddings
-    • Extract entities from different modalities
-    • Detect relationships across modalities
-    • Build multi-modal knowledge graphs
-    • Perform cross-modal similarity search
-    """
-    
-    def __init__(self):
-        """
-        Initialize multi-modal processing example.
-        
-        • Setup Semantica framework
-        • Configure multi-modal processors
-        • Initialize cross-modal embedding generation
-        • Setup multi-modal entity extraction
-        • Configure cross-modal relationship detection
-        • Setup multi-modal knowledge graph building
-        """
-        # TODO: Initialize Semantica framework
-        # TODO: Setup multi-modal data processors
-        # TODO: Configure cross-modal embedding generation
-        # TODO: Initialize multi-modal entity extraction
-        # TODO: Setup cross-modal relationship detection
-        # TODO: Configure multi-modal knowledge graph building
-        pass
-    
-    def process_multimodal_data(self, data_sources, **options):
-        """
-        Process multi-modal data from various sources.
-        
-        • Ingest data from multiple modalities
-        • Process each modality appropriately
-        • Generate cross-modal embeddings
-        • Extract entities from all modalities
-        • Detect relationships across modalities
-        • Build multi-modal knowledge graph
-        
-        Args:
-            data_sources: Dictionary of data sources by modality
-            **options: Multi-modal processing options
-            
-        Returns:
-            dict: Multi-modal processing results
-        """
-        # TODO: Ingest data from multiple modalities
-        # TODO: Process each modality using appropriate processors
-        # TODO: Generate cross-modal embeddings using MultiModalEmbedder
-        # TODO: Extract entities from all modalities
-        # TODO: Detect relationships across modalities
-        # TODO: Build multi-modal knowledge graph
-        # TODO: Return multi-modal processing results
-        pass
-    
-    def process_text_and_images(self, text_data, image_data, **options):
-        """
-        Process text and image data together.
-        
-        • Process text content
-        • Process image content
-        • Generate cross-modal embeddings
-        • Extract entities from both modalities
-        • Detect relationships between text and images
-        • Build cross-modal knowledge graph
-        
-        Args:
-            text_data: Text data to process
-            image_data: Image data to process
-            **options: Text-image processing options
-            
-        Returns:
-            dict: Text-image processing results
-        """
-        # TODO: Process text content using TextEmbedder
-        # TODO: Process image content using ImageEmbedder
-        # TODO: Generate cross-modal embeddings
-        # TODO: Extract entities from text and images
-        # TODO: Detect relationships between text and images
-        # TODO: Build cross-modal knowledge graph
-        # TODO: Return text-image processing results
-        pass
-    
-    def process_audio_and_text(self, audio_data, text_data, **options):
-        """
-        Process audio and text data together.
-        
-        • Process audio content
-        • Process text content
-        • Generate cross-modal embeddings
-        • Extract entities from both modalities
-        • Detect relationships between audio and text
-        • Build cross-modal knowledge graph
-        
-        Args:
-            audio_data: Audio data to process
-            text_data: Text data to process
-            **options: Audio-text processing options
-            
-        Returns:
-            dict: Audio-text processing results
-        """
-        # TODO: Process audio content using AudioEmbedder
-        # TODO: Process text content using TextEmbedder
-        # TODO: Generate cross-modal embeddings
-        # TODO: Extract entities from audio and text
-        # TODO: Detect relationships between audio and text
-        # TODO: Build cross-modal knowledge graph
-        # TODO: Return audio-text processing results
-        pass
-    
-    def generate_cross_modal_embeddings(self, multimodal_data, **options):
-        """
-        Generate cross-modal embeddings.
-        
-        • Process data from multiple modalities
-        • Generate embeddings for each modality
-        • Align embeddings across modalities
-        • Fuse embeddings from different modalities
-        • Return cross-modal embeddings
-        
-        Args:
-            multimodal_data: Multi-modal data to process
-            **options: Cross-modal embedding options
-            
-        Returns:
-            dict: Cross-modal embedding results
-        """
-        # TODO: Use MultiModalEmbedder to generate embeddings
-        # TODO: Align embeddings across modalities
-        # TODO: Fuse embeddings from different modalities
-        # TODO: Return cross-modal embeddings
-        pass
-    
-    def extract_cross_modal_entities(self, multimodal_data, **options):
-        """
-        Extract entities from multiple modalities.
-        
-        • Process each modality for entity extraction
-        • Align entities across modalities
-        • Resolve cross-modal entity conflicts
-        • Return cross-modal entity extraction results
-        
-        Args:
-            multimodal_data: Multi-modal data to process
-            **options: Cross-modal entity extraction options
-            
-        Returns:
-            dict: Cross-modal entity extraction results
-        """
-        # TODO: Extract entities from each modality
-        # TODO: Align entities across modalities
-        # TODO: Resolve cross-modal entity conflicts
-        # TODO: Return cross-modal entity extraction results
-        pass
-    
-    def detect_cross_modal_relationships(self, multimodal_data, entities, **options):
-        """
-        Detect relationships across modalities.
-        
-        • Process each modality for relationship extraction
-        • Detect relationships within modalities
-        • Detect relationships across modalities
-        • Validate cross-modal relationships
-        • Return cross-modal relationship detection results
-        
-        Args:
-            multimodal_data: Multi-modal data to process
-            entities: Extracted entities
-            **options: Cross-modal relationship detection options
-            
-        Returns:
-            dict: Cross-modal relationship detection results
-        """
-        # TODO: Extract relationships within each modality
-        # TODO: Detect relationships across modalities
-        # TODO: Validate cross-modal relationships
-        # TODO: Return cross-modal relationship detection results
-        pass
-    
-    def build_multimodal_knowledge_graph(self, multimodal_data, entities, relationships, **options):
-        """
-        Build multi-modal knowledge graph.
-        
-        • Integrate entities from all modalities
-        • Integrate relationships from all modalities
-        • Resolve cross-modal conflicts
-        • Build unified knowledge graph
-        • Return multi-modal knowledge graph
-        
-        Args:
-            multimodal_data: Multi-modal data
-            entities: Extracted entities
-            relationships: Extracted relationships
-            **options: Multi-modal knowledge graph building options
-            
-        Returns:
-            dict: Multi-modal knowledge graph building results
-        """
-        # TODO: Integrate entities from all modalities
-        # TODO: Integrate relationships from all modalities
-        # TODO: Resolve cross-modal conflicts
-        # TODO: Build unified knowledge graph
-        # TODO: Return multi-modal knowledge graph building results
-        pass
-    
-    def perform_cross_modal_similarity_search(self, query, multimodal_data, **options):
-        """
-        Perform cross-modal similarity search.
-        
-        • Process query across modalities
-        • Generate cross-modal embeddings
-        • Perform similarity search
-        • Return cross-modal search results
-        
-        Args:
-            query: Search query
-            multimodal_data: Multi-modal data to search
-            **options: Cross-modal search options
-            
-        Returns:
-            dict: Cross-modal search results
-        """
-        # TODO: Process query across modalities
-        # TODO: Generate cross-modal embeddings
-        # TODO: Perform similarity search
-        # TODO: Return cross-modal search results
-        pass
-
-
-def run_multimodal_processing_example():
-    """
-    Run the multi-modal processing example.
-    
-    This function demonstrates the complete multi-modal processing workflow.
-    """
-    # TODO: Create MultiModalProcessingExample instance
-    # TODO: Define multi-modal data sources
-    # TODO: Process multi-modal data
-    # TODO: Extract cross-modal entities and relationships
-    # TODO: Build multi-modal knowledge graph
-    # TODO: Export results
-    # TODO: Display results
-    pass
-
-
-def run_text_image_processing_example():
-    """
-    Run text-image processing example.
-    
-    This function demonstrates text-image cross-modal processing.
-    """
-    # TODO: Create MultiModalProcessingExample instance
-    # TODO: Define text and image data
-    # TODO: Process text and image data
-    # TODO: Extract cross-modal entities and relationships
-    # TODO: Build cross-modal knowledge graph
-    # TODO: Export results
-    # TODO: Display results
-    pass
-
-
-def run_audio_text_processing_example():
-    """
-    Run audio-text processing example.
-    
-    This function demonstrates audio-text cross-modal processing.
-    """
-    # TODO: Create MultiModalProcessingExample instance
-    # TODO: Define audio and text data
-    # TODO: Process audio and text data
-    # TODO: Extract cross-modal entities and relationships
-    # TODO: Build cross-modal knowledge graph
-    # TODO: Export results
-    # TODO: Display results
-    pass

cookbook/basic_examples/__init__.py

@@ -1,18 +0,0 @@
-"""
-Basic Examples
-
-This module contains basic usage examples for the Semantica framework.
-
-Examples:
-    - document_processing_example: Process documents and extract knowledge
-    - web_scraping_example: Scrape web content and build knowledge base
-    - knowledge_graph_example: Build and query knowledge graphs
-    - entity_extraction_example: Extract entities from text
-    - relationship_extraction_example: Extract relationships between entities
-"""
-
-# from .document_processing_example import DocumentProcessingExample
-# from .web_scraping_example import WebScrapingExample
-# from .knowledge_graph_example import KnowledgeGraphExample
-# from .entity_extraction_example import EntityExtractionExample
-# from .relationship_extraction_example import RelationshipExtractionExample

cookbook/basic_examples/document_processing_example.py

@@ -1,216 +0,0 @@
-"""
-Document Processing Example
-
-This example demonstrates how to process documents and extract knowledge using Semantica.
-
-Key Features Demonstrated:
-    - Document ingestion from various sources
-    - Document parsing and content extraction
-    - Text normalization and cleaning
-    - Entity extraction and relationship detection
-    - Knowledge graph construction
-    - Export to various formats
-
-Use Cases:
-    - Academic paper processing
-    - Legal document analysis
-    - Technical documentation processing
-    - Research paper knowledge extraction
-"""
-
-
-class DocumentProcessingExample:
-    """
-    Document processing example implementation.
-    
-    This example shows how to:
-    • Ingest documents from various sources
-    • Parse and extract content from different formats
-    • Normalize and clean text content
-    • Extract entities and relationships
-    • Build knowledge graphs
-    • Export results to various formats
-    """
-    
-    def __init__(self):
-        """
-        Initialize document processing example.
-        
-        • Setup Semantica framework
-        • Configure document processing pipeline
-        • Initialize knowledge extraction tools
-        • Setup export handlers
-        """
-        # TODO: Initialize Semantica framework
-        # TODO: Setup document processing pipeline
-        # TODO: Configure entity extraction
-        # TODO: Setup relationship detection
-        # TODO: Initialize knowledge graph builder
-        pass
-    
-    def process_documents(self, document_paths, **options):
-        """
-        Process documents and extract knowledge.
-        
-        • Ingest documents from specified paths
-        • Parse documents and extract content
-        • Normalize and clean text content
-        • Extract entities and relationships
-        • Build knowledge graph
-        • Export results to various formats
-        
-        Args:
-            document_paths: List of document file paths
-            **options: Additional processing options
-            
-        Returns:
-            dict: Processing results including knowledge graph and metrics
-        """
-        # TODO: Ingest documents using FileIngestor
-        # TODO: Parse documents using DocumentParser
-        # TODO: Normalize text using TextNormalizer
-        # TODO: Extract entities using NamedEntityRecognizer
-        # TODO: Extract relationships using RelationExtractor
-        # TODO: Build knowledge graph using KnowledgeGraphBuilder
-        # TODO: Export results using various exporters
-        # TODO: Return processing results
-        pass
-    
-    def process_pdf_documents(self, pdf_paths, **options):
-        """
-        Process PDF documents specifically.
-        
-        • Handle PDF-specific parsing
-        • Extract text and metadata
-        • Process embedded content
-        • Handle multi-page documents
-        
-        Args:
-            pdf_paths: List of PDF file paths
-            **options: PDF processing options
-            
-        Returns:
-            dict: PDF processing results
-        """
-        # TODO: Use PDFParser for PDF-specific processing
-        # TODO: Extract text content and metadata
-        # TODO: Handle embedded images and tables
-        # TODO: Process multi-page documents
-        # TODO: Return PDF processing results
-        pass
-    
-    def process_word_documents(self, docx_paths, **options):
-        """
-        Process Word documents specifically.
-        
-        • Handle DOCX-specific parsing
-        • Extract text and formatting
-        • Process embedded objects
-        • Handle document structure
-        
-        Args:
-            docx_paths: List of DOCX file paths
-            **options: DOCX processing options
-            
-        Returns:
-            dict: DOCX processing results
-        """
-        # TODO: Use DOCXParser for DOCX-specific processing
-        # TODO: Extract text content and formatting
-        # TODO: Handle embedded objects and tables
-        # TODO: Process document structure
-        # TODO: Return DOCX processing results
-        pass
-    
-    def extract_knowledge(self, processed_documents, **options):
-        """
-        Extract knowledge from processed documents.
-        
-        • Extract entities and relationships
-        • Build knowledge graph
-        • Identify knowledge patterns
-        • Generate knowledge insights
-        
-        Args:
-            processed_documents: Processed document data
-            **options: Knowledge extraction options
-            
-        Returns:
-            dict: Knowledge extraction results
-        """
-        # TODO: Extract entities using NamedEntityRecognizer
-        # TODO: Extract relationships using RelationExtractor
-        # TODO: Build knowledge graph using KnowledgeGraphBuilder
-        # TODO: Identify knowledge patterns
-        # TODO: Generate knowledge insights
-        # TODO: Return knowledge extraction results
-        pass
-    
-    def export_results(self, knowledge_graph, export_formats, **options):
-        """
-        Export knowledge graph to various formats.
-        
-        • Export to RDF formats
-        • Export to JSON formats
-        • Export to CSV formats
-        • Generate reports
-        
-        Args:
-            knowledge_graph: Knowledge graph to export
-            export_formats: List of export formats
-            **options: Export options
-            
-        Returns:
-            dict: Export results
-        """
-        # TODO: Export to RDF using RDFExporter
-        # TODO: Export to JSON using JSONExporter
-        # TODO: Export to CSV using CSVExporter
-        # TODO: Generate reports using ReportGenerator
-        # TODO: Return export results
-        pass
-
-
-def run_document_processing_example():
-    """
-    Run the document processing example.
-    
-    This function demonstrates the complete document processing workflow.
-    """
-    # TODO: Create DocumentProcessingExample instance
-    # TODO: Define document paths
-    # TODO: Process documents
-    # TODO: Extract knowledge
-    # TODO: Export results
-    # TODO: Display results
-    pass
-
-
-def run_pdf_processing_example():
-    """
-    Run PDF document processing example.
-    
-    This function demonstrates PDF-specific processing.
-    """
-    # TODO: Create DocumentProcessingExample instance
-    # TODO: Define PDF document paths
-    # TODO: Process PDF documents
-    # TODO: Extract knowledge from PDFs
-    # TODO: Export PDF processing results
-    # TODO: Display results
-    pass
-
-
-def run_word_processing_example():
-    """
-    Run Word document processing example.
-    
-    This function demonstrates DOCX-specific processing.
-    """
-    # TODO: Create DocumentProcessingExample instance
-    # TODO: Define DOCX document paths
-    # TODO: Process DOCX documents
-    # TODO: Extract knowledge from DOCX files
-    # TODO: Export DOCX processing results
-    # TODO: Display results
-    pass

cookbook/basic_examples/knowledge_graph_example.py

@@ -1,295 +0,0 @@
-"""
-Knowledge Graph Example
-
-This example demonstrates how to build and query knowledge graphs using Semantica.
-
-Key Features Demonstrated:
-    - Knowledge graph construction
-    - Entity resolution and deduplication
-    - Relationship extraction and validation
-    - Graph analytics and centrality measures
-    - Knowledge graph querying
-    - Graph visualization and export
-
-Use Cases:
-    - Enterprise knowledge management
-    - Research knowledge organization
-    - Domain-specific knowledge graphs
-    - Knowledge discovery and exploration
-    - Knowledge graph analytics
-"""
-
-
-class KnowledgeGraphExample:
-    """
-    Knowledge graph example implementation.
-    
-    This example shows how to:
-    • Build knowledge graphs from various data sources
-    • Resolve entities and handle deduplication
-    • Extract and validate relationships
-    • Perform graph analytics and centrality analysis
-    • Query knowledge graphs
-    • Export and visualize graphs
-    """
-    
-    def __init__(self):
-        """
-        Initialize knowledge graph example.
-        
-        • Setup Semantica framework
-        • Configure knowledge graph builder
-        • Initialize entity resolution
-        • Setup relationship extraction
-        • Configure graph analytics
-        • Setup query engines
-        """
-        # TODO: Initialize Semantica framework
-        # TODO: Setup KnowledgeGraphBuilder
-        # TODO: Configure EntityResolver
-        # TODO: Setup RelationExtractor
-        # TODO: Initialize GraphAnalyzer
-        # TODO: Setup query engines
-        pass
-    
-    def build_knowledge_graph(self, data_sources, **options):
-        """
-        Build knowledge graph from data sources.
-        
-        • Process data from various sources
-        • Extract entities and relationships
-        • Resolve entity duplicates
-        • Build knowledge graph structure
-        • Validate graph consistency
-        • Return knowledge graph
-        
-        Args:
-            data_sources: List of data sources to process
-            **options: Knowledge graph building options
-            
-        Returns:
-            dict: Knowledge graph building results
-        """
-        # TODO: Process data sources using appropriate ingestors
-        # TODO: Extract entities using NamedEntityRecognizer
-        # TODO: Extract relationships using RelationExtractor
-        # TODO: Resolve entities using EntityResolver
-        # TODO: Build knowledge graph using KnowledgeGraphBuilder
-        # TODO: Validate graph consistency
-        # TODO: Return knowledge graph
-        pass
-    
-    def resolve_entities(self, entities, **options):
-        """
-        Resolve entity duplicates and conflicts.
-        
-        • Identify duplicate entities
-        • Resolve entity conflicts
-        • Merge duplicate entities
-        • Update entity references
-        • Return resolved entities
-        
-        Args:
-            entities: List of entities to resolve
-            **options: Entity resolution options
-            
-        Returns:
-            dict: Entity resolution results
-        """
-        # TODO: Use EntityResolver to identify duplicates
-        # TODO: Resolve entity conflicts
-        # TODO: Merge duplicate entities
-        # TODO: Update entity references
-        # TODO: Return resolved entities
-        pass
-    
-    def extract_relationships(self, entities, text_content, **options):
-        """
-        Extract relationships between entities.
-        
-        • Process text content for relationships
-        • Extract relationships between entities
-        • Validate relationship quality
-        • Classify relationship types
-        • Return relationship extraction results
-        
-        Args:
-            entities: List of entities
-            text_content: Text content to process
-            **options: Relationship extraction options
-            
-        Returns:
-            dict: Relationship extraction results
-        """
-        # TODO: Use RelationExtractor to extract relationships
-        # TODO: Validate relationship quality
-        # TODO: Classify relationship types
-        # TODO: Return relationship extraction results
-        pass
-    
-    def analyze_graph_centrality(self, knowledge_graph, **options):
-        """
-        Analyze graph centrality measures.
-        
-        • Calculate degree centrality
-        • Calculate betweenness centrality
-        • Calculate closeness centrality
-        • Calculate eigenvector centrality
-        • Return centrality analysis results
-        
-        Args:
-            knowledge_graph: Knowledge graph to analyze
-            **options: Centrality analysis options
-            
-        Returns:
-            dict: Centrality analysis results
-        """
-        # TODO: Use GraphAnalyzer to calculate centrality measures
-        # TODO: Calculate degree centrality
-        # TODO: Calculate betweenness centrality
-        # TODO: Calculate closeness centrality
-        # TODO: Calculate eigenvector centrality
-        # TODO: Return centrality analysis results
-        pass
-    
-    def detect_communities(self, knowledge_graph, **options):
-        """
-        Detect communities in knowledge graph.
-        
-        • Apply community detection algorithms
-        • Identify community structures
-        • Calculate community metrics
-        • Handle overlapping communities
-        • Return community detection results
-        
-        Args:
-            knowledge_graph: Knowledge graph to analyze
-            **options: Community detection options
-            
-        Returns:
-            dict: Community detection results
-        """
-        # TODO: Use GraphAnalyzer to detect communities
-        # TODO: Apply community detection algorithms
-        # TODO: Identify community structures
-        # TODO: Calculate community metrics
-        # TODO: Return community detection results
-        pass
-    
-    def analyze_connectivity(self, knowledge_graph, **options):
-        """
-        Analyze graph connectivity and structure.
-        
-        • Calculate connectivity metrics
-        • Identify connected components
-        • Analyze path lengths and distances
-        • Detect bottlenecks and bridges
-        • Return connectivity analysis
-        
-        Args:
-            knowledge_graph: Knowledge graph to analyze
-            **options: Connectivity analysis options
-            
-        Returns:
-            dict: Connectivity analysis results
-        """
-        # TODO: Use GraphAnalyzer to analyze connectivity
-        # TODO: Calculate connectivity metrics
-        # TODO: Identify connected components
-        # TODO: Analyze path lengths and distances
-        # TODO: Detect bottlenecks and bridges
-        # TODO: Return connectivity analysis results
-        pass
-    
-    def query_knowledge_graph(self, knowledge_graph, query, **options):
-        """
-        Query knowledge graph using various query languages.
-        
-        • Process SPARQL queries
-        • Process natural language queries
-        • Process graph traversal queries
-        • Return query results
-        
-        Args:
-            knowledge_graph: Knowledge graph to query
-            query: Query to execute
-            **options: Query options
-            
-        Returns:
-            dict: Query results
-        """
-        # TODO: Use query engines to process queries
-        # TODO: Process SPARQL queries
-        # TODO: Process natural language queries
-        # TODO: Process graph traversal queries
-        # TODO: Return query results
-        pass
-    
-    def export_knowledge_graph(self, knowledge_graph, export_formats, **options):
-        """
-        Export knowledge graph to various formats.
-        
-        • Export to RDF formats
-        • Export to JSON formats
-        • Export to graph formats
-        • Generate visualization files
-        
-        Args:
-            knowledge_graph: Knowledge graph to export
-            export_formats: List of export formats
-            **options: Export options
-            
-        Returns:
-            dict: Export results
-        """
-        # TODO: Use various exporters to export knowledge graph
-        # TODO: Export to RDF formats using RDFExporter
-        # TODO: Export to JSON formats using JSONExporter
-        # TODO: Export to graph formats using GraphExporter
-        # TODO: Generate visualization files
-        # TODO: Return export results
-        pass
-
-
-def run_knowledge_graph_example():
-    """
-    Run the knowledge graph example.
-    
-    This function demonstrates the complete knowledge graph workflow.
-    """
-    # TODO: Create KnowledgeGraphExample instance
-    # TODO: Define data sources
-    # TODO: Build knowledge graph
-    # TODO: Analyze graph properties
-    # TODO: Query knowledge graph
-    # TODO: Export results
-    # TODO: Display results
-    pass
-
-
-def run_centrality_analysis_example():
-    """
-    Run centrality analysis example.
-    
-    This function demonstrates graph centrality analysis.
-    """
-    # TODO: Create KnowledgeGraphExample instance
-    # TODO: Build knowledge graph
-    # TODO: Analyze centrality measures
-    # TODO: Display centrality results
-    # TODO: Export centrality analysis
-    pass
-
-
-def run_community_detection_example():
-    """
-    Run community detection example.
-    
-    This function demonstrates community detection in knowledge graphs.
-    """
-    # TODO: Create KnowledgeGraphExample instance
-    # TODO: Build knowledge graph
-    # TODO: Detect communities
-    # TODO: Display community results
-    # TODO: Export community analysis
-    pass

cookbook/basic_examples/web_scraping_example.py

@@ -1,266 +0,0 @@
-"""
-Web Scraping Example
-
-This example demonstrates how to scrape web content and build knowledge bases using Semantica.
-
-Key Features Demonstrated:
-    - Web content scraping and crawling
-    - HTML content parsing and cleaning
-    - Web content normalization
-    - Entity extraction from web content
-    - Relationship detection in web content
-    - Knowledge base construction from web data
-
-Use Cases:
-    - News article processing
-    - Blog post analysis
-    - Website content extraction
-    - Social media content processing
-    - E-commerce product information extraction
-"""
-
-
-class WebScrapingExample:
-    """
-    Web scraping example implementation.
-    
-    This example shows how to:
-    • Scrape web content from various sources
-    • Parse HTML content and extract text
-    • Normalize and clean web content
-    • Extract entities and relationships
-    • Build knowledge bases from web data
-    • Handle different web content types
-    """
-    
-    def __init__(self):
-        """
-        Initialize web scraping example.
-        
-        • Setup Semantica framework
-        • Configure web scraping tools
-        • Initialize content parsing
-        • Setup knowledge extraction
-        • Configure export handlers
-        """
-        # TODO: Initialize Semantica framework
-        # TODO: Setup WebIngestor for web scraping
-        # TODO: Configure HTML parsing
-        # TODO: Setup content normalization
-        # TODO: Initialize entity extraction
-        # TODO: Setup relationship detection
-        pass
-    
-    def scrape_website(self, website_url, **options):
-        """
-        Scrape content from a website.
-        
-        • Scrape web content from specified URL
-        • Parse HTML content and extract text
-        • Normalize and clean content
-        • Extract entities and relationships
-        • Build knowledge base
-        • Export results
-        
-        Args:
-            website_url: URL of website to scrape
-            **options: Scraping options
-            
-        Returns:
-            dict: Scraping results including knowledge base
-        """
-        # TODO: Use WebIngestor to scrape website
-        # TODO: Parse HTML content using WebParser
-        # TODO: Normalize content using TextNormalizer
-        # TODO: Extract entities using NamedEntityRecognizer
-        # TODO: Extract relationships using RelationExtractor
-        # TODO: Build knowledge base
-        # TODO: Return scraping results
-        pass
-    
-    def scrape_multiple_pages(self, page_urls, **options):
-        """
-        Scrape content from multiple web pages.
-        
-        • Scrape multiple pages concurrently
-        • Process each page individually
-        • Aggregate results from all pages
-        • Build comprehensive knowledge base
-        
-        Args:
-            page_urls: List of page URLs to scrape
-            **options: Scraping options
-            
-        Returns:
-            dict: Aggregated scraping results
-        """
-        # TODO: Use WebIngestor to scrape multiple pages
-        # TODO: Process each page concurrently
-        # TODO: Aggregate results from all pages
-        # TODO: Build comprehensive knowledge base
-        # TODO: Return aggregated results
-        pass
-    
-    def scrape_with_sitemap(self, sitemap_url, **options):
-        """
-        Scrape website using sitemap.
-        
-        • Parse sitemap to get page URLs
-        • Scrape all pages in sitemap
-        • Process sitemap structure
-        • Build comprehensive knowledge base
-        
-        Args:
-            sitemap_url: URL of website sitemap
-            **options: Scraping options
-            
-        Returns:
-            dict: Sitemap-based scraping results
-        """
-        # TODO: Use SitemapCrawler to parse sitemap
-        # TODO: Extract page URLs from sitemap
-        # TODO: Scrape all pages in sitemap
-        # TODO: Process sitemap structure
-        # TODO: Build comprehensive knowledge base
-        # TODO: Return sitemap-based results
-        pass
-    
-    def process_news_articles(self, news_urls, **options):
-        """
-        Process news articles from web sources.
-        
-        • Scrape news article content
-        • Extract article metadata
-        • Process article text content
-        • Extract entities and relationships
-        • Build news knowledge base
-        
-        Args:
-            news_urls: List of news article URLs
-            **options: News processing options
-            
-        Returns:
-            dict: News processing results
-        """
-        # TODO: Scrape news article content
-        # TODO: Extract article metadata (title, date, author)
-        # TODO: Process article text content
-        # TODO: Extract entities and relationships
-        # TODO: Build news knowledge base
-        # TODO: Return news processing results
-        pass
-    
-    def process_blog_posts(self, blog_urls, **options):
-        """
-        Process blog posts from web sources.
-        
-        • Scrape blog post content
-        • Extract post metadata
-        • Process post text content
-        • Extract entities and relationships
-        • Build blog knowledge base
-        
-        Args:
-            blog_urls: List of blog post URLs
-            **options: Blog processing options
-            
-        Returns:
-            dict: Blog processing results
-        """
-        # TODO: Scrape blog post content
-        # TODO: Extract post metadata (title, date, author, tags)
-        # TODO: Process post text content
-        # TODO: Extract entities and relationships
-        # TODO: Build blog knowledge base
-        # TODO: Return blog processing results
-        pass
-    
-    def extract_web_entities(self, web_content, **options):
-        """
-        Extract entities from web content.
-        
-        • Process web content for entity extraction
-        • Extract named entities
-        • Classify entity types
-        • Handle entity disambiguation
-        
-        Args:
-            web_content: Web content to process
-            **options: Entity extraction options
-            
-        Returns:
-            dict: Entity extraction results
-        """
-        # TODO: Use NamedEntityRecognizer to extract entities
-        # TODO: Classify entity types
-        # TODO: Handle entity disambiguation
-        # TODO: Return entity extraction results
-        pass
-    
-    def extract_web_relationships(self, web_content, entities, **options):
-        """
-        Extract relationships from web content.
-        
-        • Process web content for relationship extraction
-        • Extract relationships between entities
-        • Classify relationship types
-        • Handle relationship disambiguation
-        
-        Args:
-            web_content: Web content to process
-            entities: Extracted entities
-            **options: Relationship extraction options
-            
-        Returns:
-            dict: Relationship extraction results
-        """
-        # TODO: Use RelationExtractor to extract relationships
-        # TODO: Classify relationship types
-        # TODO: Handle relationship disambiguation
-        # TODO: Return relationship extraction results
-        pass
-
-
-def run_web_scraping_example():
-    """
-    Run the web scraping example.
-    
-    This function demonstrates the complete web scraping workflow.
-    """
-    # TODO: Create WebScrapingExample instance
-    # TODO: Define website URLs to scrape
-    # TODO: Scrape website content
-    # TODO: Extract knowledge from web content
-    # TODO: Export results
-    # TODO: Display results
-    pass
-
-
-def run_news_scraping_example():
-    """
-    Run news article scraping example.
-    
-    This function demonstrates news article processing.
-    """
-    # TODO: Create WebScrapingExample instance
-    # TODO: Define news article URLs
-    # TODO: Scrape news articles
-    # TODO: Extract knowledge from news
-    # TODO: Export news processing results
-    # TODO: Display results
-    pass
-
-
-def run_blog_scraping_example():
-    """
-    Run blog post scraping example.
-    
-    This function demonstrates blog post processing.
-    """
-    # TODO: Create WebScrapingExample instance
-    # TODO: Define blog post URLs
-    # TODO: Scrape blog posts
-    # TODO: Extract knowledge from blog posts
-    # TODO: Export blog processing results
-    # TODO: Display results
-    pass

cookbook/examples/01_quick_start/__init__.py

@@ -1,6 +0,0 @@
-"""
-Quick start examples for Semantica framework.
-
-This module contains basic examples to get started
-with the Semantica framework.
-"""

cookbook/examples/01_quick_start/knowledge_graph_101.py

@@ -1,14 +0,0 @@
-"""
-Knowledge graph 101 example for Semantica framework.
-
-This example demonstrates basic knowledge graph construction
-using the Semantica framework.
-"""
-
-# TODO: Implement knowledge graph 101 example
-# - Basic knowledge graph construction
-# - Entity and relationship extraction
-# - Graph visualization and analysis
-# - Example usage and documentation
-# - Performance optimization tips
-# - Best practices and guidelines

cookbook/examples/01_quick_start/semantic_search.py

@@ -1,14 +0,0 @@
-"""
-Semantic search example for Semantica framework.
-
-This example demonstrates basic semantic search
-using the Semantica framework.
-"""
-
-# TODO: Implement semantic search example
-# - Basic semantic search implementation
-# - Vector similarity search
-# - Query processing and ranking
-# - Example usage and documentation
-# - Performance optimization tips
-# - Advanced search features

cookbook/examples/01_quick_start/simple_processing.py

@@ -1,14 +0,0 @@
-"""
-Simple document processing example for Semantica framework.
-
-This example demonstrates basic document processing
-using the Semantica framework.
-"""
-
-# TODO: Implement simple processing example
-# - Basic document ingestion and parsing
-# - Text normalization and cleaning
-# - Simple semantic extraction
-# - Basic knowledge graph construction
-# - Example usage and documentation
-# - Performance optimization tips

cookbook/examples/02_document_processing/__init__.py

@@ -1,6 +0,0 @@
-"""
-Document processing examples for Semantica framework.
-
-This module contains examples for processing various
-document formats using the Semantica framework.
-"""

cookbook/examples/02_document_processing/multi_format.py

@@ -1,14 +0,0 @@
-"""
-Multi-format processing example for Semantica framework.
-
-This example demonstrates processing multiple document formats
-using the Semantica framework.
-"""
-
-# TODO: Implement multi-format processing example
-# - Multiple document format processing
-# - Format detection and routing
-# - Unified processing pipeline
-# - Example usage and documentation
-# - Performance optimization tips
-# - Advanced multi-format features

cookbook/examples/02_document_processing/office_documents.py

@@ -1,14 +0,0 @@
-"""
-Office documents example for Semantica framework.
-
-This example demonstrates processing of DOCX, XLSX, and PPTX
-documents using the Semantica framework.
-"""
-
-# TODO: Implement office documents example
-# - DOCX, XLSX, and PPTX processing
-# - Document structure extraction
-# - Content and formatting preservation
-# - Example usage and documentation
-# - Performance optimization tips
-# - Advanced document features

cookbook/examples/02_document_processing/pdf_extraction.py

@@ -1,14 +0,0 @@
-"""
-PDF extraction example for Semantica framework.
-
-This example demonstrates PDF document processing
-using the Semantica framework.
-"""
-
-# TODO: Implement PDF extraction example
-# - PDF document ingestion and parsing
-# - Text and table extraction
-# - Image and OCR processing
-# - Metadata extraction
-# - Example usage and documentation
-# - Performance optimization tips

cookbook/examples/03_knowledge_graphs/__init__.py

@@ -1,6 +0,0 @@
-"""
-Knowledge graphs examples for Semantica framework.
-
-This module contains examples for building and managing
-knowledge graphs using the Semantica framework.
-"""

cookbook/examples/03_knowledge_graphs/building_kg.py

@@ -1,14 +0,0 @@
-"""
-Building knowledge graph example for Semantica framework.
-
-This example demonstrates complete knowledge graph construction
-using the Semantica framework.
-"""
-
-# TODO: Implement building knowledge graph example
-# - Complete knowledge graph construction
-# - Entity and relationship extraction
-# - Graph building and optimization
-# - Example usage and documentation
-# - Performance optimization tips
-# - Advanced graph features

cookbook/examples/03_knowledge_graphs/conflict_detection.py

@@ -1,14 +0,0 @@
-"""
-Conflict detection example for Semantica framework.
-
-This example demonstrates handling conflicts and inconsistencies
-in knowledge graphs using the Semantica framework.
-"""
-
-# TODO: Implement conflict detection example
-# - Conflict identification and detection
-# - Inconsistency resolution strategies
-# - Conflict resolution and merging
-# - Example usage and documentation
-# - Performance optimization tips
-# - Advanced conflict handling

cookbook/examples/03_knowledge_graphs/entity_resolution.py

@@ -1,14 +0,0 @@
-"""
-Entity resolution example for Semantica framework.
-
-This example demonstrates entity disambiguation and resolution
-using the Semantica framework.
-"""
-
-# TODO: Implement entity resolution example
-# - Entity disambiguation and resolution
-# - Similarity-based entity matching
-# - Entity merging and consolidation
-# - Example usage and documentation
-# - Performance optimization tips
-# - Advanced resolution techniques

cookbook/examples/04_semantic_search/__init__.py

@@ -1,6 +0,0 @@
-"""
-Semantic search examples for Semantica framework.
-
-This module contains examples for implementing semantic search
-using the Semantica framework.
-"""

cookbook/examples/04_semantic_search/hybrid_search.py

@@ -1,14 +0,0 @@
-"""
-Hybrid search example for Semantica framework.
-
-This example demonstrates combined vector and metadata search
-using the Semantica framework.
-"""
-
-# TODO: Implement hybrid search example
-# - Combined vector and metadata search
-# - Result fusion and ranking
-# - Advanced search strategies
-# - Example usage and documentation
-# - Performance optimization tips
-# - Advanced hybrid features

cookbook/examples/04_semantic_search/rag_pipeline.py

@@ -1,14 +0,0 @@
-"""
-RAG pipeline example for Semantica framework.
-
-This example demonstrates RAG (Retrieval-Augmented Generation)
-implementation using the Semantica framework.
-"""
-
-# TODO: Implement RAG pipeline example
-# - RAG pipeline implementation
-# - Document retrieval and generation
-# - LLM integration and optimization
-# - Example usage and documentation
-# - Performance optimization tips
-# - Advanced RAG features

cookbook/examples/04_semantic_search/vector_search.py

@@ -1,14 +0,0 @@
-"""
-Vector search example for Semantica framework.
-
-This example demonstrates vector similarity search
-using the Semantica framework.
-"""
-
-# TODO: Implement vector search example
-# - Vector similarity search implementation
-# - Embedding generation and indexing
-# - Search optimization and ranking
-# - Example usage and documentation
-# - Performance optimization tips
-# - Advanced search features

cookbook/examples/05_real_time_streams/__init__.py

@@ -1,6 +0,0 @@
-"""
-Real-time streams examples for Semantica framework.
-
-This module contains examples for real-time stream processing
-using the Semantica framework.
-"""

cookbook/examples/05_real_time_streams/feed_monitoring.py

@@ -1,14 +0,0 @@
-"""
-Feed monitoring example for Semantica framework.
-
-This example demonstrates RSS feed monitoring
-using the Semantica framework.
-"""
-
-# TODO: Implement feed monitoring example
-# - RSS feed monitoring implementation
-# - Real-time feed processing and analysis
-# - Change detection and notification
-# - Example usage and documentation
-# - Performance optimization tips
-# - Advanced monitoring features

cookbook/examples/05_real_time_streams/kafka_processing.py

@@ -1,14 +0,0 @@
-"""
-Kafka processing example for Semantica framework.
-
-This example demonstrates Kafka stream processing
-using the Semantica framework.
-"""
-
-# TODO: Implement Kafka processing example
-# - Kafka stream processing implementation
-# - Real-time data ingestion and processing
-# - Stream analytics and monitoring
-# - Example usage and documentation
-# - Performance optimization tips
-# - Advanced Kafka features

cookbook/examples/05_real_time_streams/web_monitoring.py

@@ -1,14 +0,0 @@
-"""
-Web monitoring example for Semantica framework.
-
-This example demonstrates website change detection
-using the Semantica framework.
-"""
-
-# TODO: Implement web monitoring example
-# - Website change detection implementation
-# - Real-time web content monitoring
-# - Change analysis and notification
-# - Example usage and documentation
-# - Performance optimization tips
-# - Advanced web monitoring features

cookbook/examples/__init__.py

@@ -1,6 +0,0 @@
-"""
-Examples module for Semantica framework.
-
-This module contains example implementations and tutorials
-for using the Semantica framework.
-"""

cookbook/notebooks/__init__.py

@@ -1,6 +0,0 @@
-"""
-Jupyter notebooks module for Semantica framework.
-
-This module contains Jupyter notebooks for tutorials
-and experiments with the Semantica framework.
-"""

cookbook/notebooks/experiment_custom_parsers.ipynb

@@ -1,29 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Custom Parser Development with Semantica Framework\n",
-        "\n",
-        "This notebook demonstrates custom parser development\n",
-        "using the Semantica framework.\n",
-        "\n",
-        "## TODO: Implement custom parser experiment\n",
-        "- Custom parser development concepts\n",
-        "- Parser interface and implementation\n",
-        "- Parser testing and validation\n",
-        "- Performance optimization tips\n",
-        "- Advanced parser features\n",
-        "- Integration and deployment\n"
-      ]
-    }
-  ],
-  "metadata": {
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

cookbook/notebooks/experiment_reasoning.ipynb

@@ -1,29 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Reasoning Experiments with Semantica Framework\n",
-        "\n",
-        "This notebook demonstrates reasoning and inference capabilities\n",
-        "of the Semantica framework.\n",
-        "\n",
-        "## TODO: Implement reasoning experiment\n",
-        "- Reasoning and inference concepts\n",
-        "- Rule-based reasoning implementation\n",
-        "- SPARQL-based reasoning\n",
-        "- Performance optimization tips\n",
-        "- Advanced reasoning features\n",
-        "- Reasoning evaluation and testing\n"
-      ]
-    }
-  ],
-  "metadata": {
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

cookbook/notebooks/tutorial_01_getting_started.ipynb

@@ -1,28 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Getting Started with Semantica Framework\n",
-        "\n",
-        "This notebook provides an introduction to the Semantica framework\n",
-        "and demonstrates basic usage patterns.\n",
-        "\n",
-        "## TODO: Implement getting started tutorial\n",
-        "- Introduction to Semantica framework\n",
-        "- Basic installation and setup\n",
-        "- Simple examples and use cases\n",
-        "- Performance optimization tips\n",
-        "- Next steps and resources\n"
-      ]
-    }
-  ],
-  "metadata": {
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

cookbook/notebooks/tutorial_02_data_ingestion.ipynb

@@ -1,29 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Data Ingestion with Semantica Framework\n",
-        "\n",
-        "This notebook demonstrates data ingestion capabilities\n",
-        "of the Semantica framework.\n",
-        "\n",
-        "## TODO: Implement data ingestion tutorial\n",
-        "- Data ingestion overview and concepts\n",
-        "- File system and cloud storage ingestion\n",
-        "- Web scraping and feed processing\n",
-        "- Real-time stream ingestion\n",
-        "- Performance optimization tips\n",
-        "- Advanced ingestion features\n"
-      ]
-    }
-  ],
-  "metadata": {
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

cookbook/notebooks/tutorial_03_semantic_extraction.ipynb

@@ -1,29 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Semantic Extraction with Semantica Framework\n",
-        "\n",
-        "This notebook demonstrates semantic extraction capabilities\n",
-        "of the Semantica framework.\n",
-        "\n",
-        "## TODO: Implement semantic extraction tutorial\n",
-        "- Semantic extraction overview and concepts\n",
-        "- Named entity recognition and classification\n",
-        "- Relationship extraction and analysis\n",
-        "- Event detection and processing\n",
-        "- Performance optimization tips\n",
-        "- Advanced extraction features\n"
-      ]
-    }
-  ],
-  "metadata": {
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

cookbook/notebooks/tutorial_04_knowledge_graphs.ipynb

@@ -1,29 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Knowledge Graphs with Semantica Framework\n",
-        "\n",
-        "This notebook demonstrates knowledge graph construction\n",
-        "using the Semantica framework.\n",
-        "\n",
-        "## TODO: Implement knowledge graphs tutorial\n",
-        "- Knowledge graph overview and concepts\n",
-        "- Graph construction and management\n",
-        "- Entity resolution and deduplication\n",
-        "- Graph analysis and visualization\n",
-        "- Performance optimization tips\n",
-        "- Advanced graph features\n"
-      ]
-    }
-  ],
-  "metadata": {
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

cookbook/notebooks/tutorial_05_embeddings.ipynb

@@ -1,29 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Embeddings and Search with Semantica Framework\n",
-        "\n",
-        "This notebook demonstrates embeddings and search capabilities\n",
-        "of the Semantica framework.\n",
-        "\n",
-        "## TODO: Implement embeddings tutorial\n",
-        "- Embeddings overview and concepts\n",
-        "- Text and image embedding generation\n",
-        "- Vector similarity search\n",
-        "- Hybrid search strategies\n",
-        "- Performance optimization tips\n",
-        "- Advanced embedding features\n"
-      ]
-    }
-  ],
-  "metadata": {
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

cookbook/notebooks/tutorial_06_advanced_pipelines.ipynb

@@ -1,29 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Advanced Pipelines with Semantica Framework\n",
-        "\n",
-        "This notebook demonstrates advanced pipeline capabilities\n",
-        "of the Semantica framework.\n",
-        "\n",
-        "## TODO: Implement advanced pipelines tutorial\n",
-        "- Advanced pipeline concepts and patterns\n",
-        "- Pipeline construction and orchestration\n",
-        "- Parallel and distributed processing\n",
-        "- Error handling and recovery\n",
-        "- Performance optimization tips\n",
-        "- Advanced pipeline features\n"
-      ]
-    }
-  ],
-  "metadata": {
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

cookbook/use_cases/__init__.py

@@ -1,6 +0,0 @@
-"""
-Use cases module for Semantica framework.
-
-This module contains domain-specific use cases and examples
-for the Semantica framework.
-"""

cookbook/use_cases/cybersecurity/__init__.py

@@ -1,6 +0,0 @@
-"""
-Cybersecurity use cases for Semantica framework.
-
-This module contains cybersecurity-specific examples and use cases
-for the Semantica framework.
-"""

cookbook/use_cases/cybersecurity/incident_analysis.py

@@ -1,14 +0,0 @@
-"""
-Incident analysis example for Semantica framework.
-
-This example demonstrates security incident analysis
-using the Semantica framework.
-"""
-
-# TODO: Implement incident analysis example
-# - Security incident processing and analysis
-# - Incident entity extraction and classification
-# - Incident correlation and insights
-# - Example usage and documentation
-# - Performance optimization tips
-# - Advanced incident analysis features

cookbook/use_cases/cybersecurity/threat_intelligence.py

@@ -1,14 +0,0 @@
-"""
-Threat intelligence example for Semantica framework.
-
-This example demonstrates threat intelligence processing
-using the Semantica framework.
-"""
-
-# TODO: Implement threat intelligence example
-# - Threat report processing and analysis
-# - Threat entity extraction and classification
-# - Threat intelligence synthesis and insights
-# - Example usage and documentation
-# - Performance optimization tips
-# - Advanced threat intelligence features

cookbook/use_cases/cybersecurity/vulnerability_tracking.py

@@ -1,14 +0,0 @@
-"""
-Vulnerability tracking example for Semantica framework.
-
-This example demonstrates vulnerability tracking
-using the Semantica framework.
-"""
-
-# TODO: Implement vulnerability tracking example
-# - CVE database integration and processing
-# - Vulnerability entity extraction and classification
-# - Vulnerability tracking and monitoring
-# - Example usage and documentation
-# - Performance optimization tips
-# - Advanced vulnerability features

cookbook/use_cases/finance/__init__.py

@@ -1,6 +0,0 @@
-"""
-Finance use cases for Semantica framework.
-
-This module contains finance-specific examples and use cases
-for the Semantica framework.
-"""

cookbook/use_cases/finance/financial_reports.py

@@ -1,14 +0,0 @@
-"""
-Financial reports processing example for Semantica framework.
-
-This example demonstrates processing financial reports
-using the Semantica framework.
-"""
-
-# TODO: Implement financial reports processing
-# - Financial document analysis and processing
-# - Financial entity and relationship extraction
-# - Report insights and analytics
-# - Example usage and documentation
-# - Performance optimization tips
-# - Advanced financial features

cookbook/use_cases/finance/market_intelligence.py

@@ -1,14 +0,0 @@
-"""
-Market intelligence example for Semantica framework.
-
-This example demonstrates market intelligence processing
-using the Semantica framework.
-"""
-
-# TODO: Implement market intelligence example
-# - News and market data processing
-# - Market trend analysis and insights
-# - Financial entity extraction and classification
-# - Example usage and documentation
-# - Performance optimization tips
-# - Advanced market intelligence features

cookbook/use_cases/finance/regulatory_compliance.py

@@ -1,14 +0,0 @@
-"""
-Regulatory compliance example for Semantica framework.
-
-This example demonstrates regulatory compliance monitoring
-using the Semantica framework.
-"""
-
-# TODO: Implement regulatory compliance example
-# - Regulatory document processing and analysis
-# - Compliance monitoring and reporting
-# - Regulatory entity extraction and tracking
-# - Example usage and documentation
-# - Performance optimization tips
-# - Advanced compliance features

cookbook/use_cases/healthcare/__init__.py

@@ -1,6 +0,0 @@
-"""
-Healthcare use cases for Semantica framework.
-
-This module contains healthcare-specific examples and use cases
-for the Semantica framework.
-"""

cookbook/use_cases/healthcare/clinical_reports.py

@@ -1,14 +0,0 @@
-"""
-Clinical reports processing example for Semantica framework.
-
-This example demonstrates processing clinical reports
-using the Semantica framework.
-"""
-
-# TODO: Implement clinical reports processing
-# - Clinical report analysis and processing
-# - Medical entity and relationship extraction
-# - Report summarization and insights
-# - Example usage and documentation
-# - Performance optimization tips
-# - Advanced clinical features

cookbook/use_cases/healthcare/drug_interactions.py

@@ -1,14 +0,0 @@
-"""
-Drug interactions example for Semantica framework.
-
-This example demonstrates drug interaction detection
-using the Semantica framework.
-"""
-
-# TODO: Implement drug interactions example
-# - Drug interaction detection and analysis
-# - Pharmacological relationship extraction
-# - Interaction risk assessment
-# - Example usage and documentation
-# - Performance optimization tips
-# - Advanced drug interaction features

cookbook/use_cases/healthcare/medical_literature.py

@@ -1,14 +0,0 @@
-"""
-Medical literature processing example for Semantica framework.
-
-This example demonstrates processing medical literature
-using the Semantica framework.
-"""
-
-# TODO: Implement medical literature processing
-# - PubMed data processing and analysis
-# - Medical entity extraction and classification
-# - Literature review and synthesis
-# - Example usage and documentation
-# - Performance optimization tips
-# - Advanced medical features

cookbook/use_cases/legal/__init__.py

@@ -1,6 +0,0 @@
-"""
-Legal use cases for Semantica framework.
-
-This module contains legal-specific examples and use cases
-for the Semantica framework.
-"""

cookbook/use_cases/legal/case_law.py

@@ -1,14 +0,0 @@
-"""
-Case law processing example for Semantica framework.
-
-This example demonstrates legal case processing
-using the Semantica framework.
-"""
-
-# TODO: Implement case law processing
-# - Legal case processing and analysis
-# - Legal entity extraction and classification
-# - Case law insights and analytics
-# - Example usage and documentation
-# - Performance optimization tips
-# - Advanced legal features

cookbook/use_cases/legal/contract_analysis.py

@@ -1,14 +0,0 @@
-"""
-Contract analysis example for Semantica framework.
-
-This example demonstrates contract intelligence
-using the Semantica framework.
-"""
-
-# TODO: Implement contract analysis example
-# - Contract processing and analysis
-# - Contract entity extraction and classification
-# - Contract insights and analytics
-# - Example usage and documentation
-# - Performance optimization tips
-# - Advanced contract features

cookbook/use_cases/legal/regulatory_mapping.py

@@ -1,14 +0,0 @@
-"""
-Regulatory mapping example for Semantica framework.
-
-This example demonstrates regulation tracking
-using the Semantica framework.
-"""
-
-# TODO: Implement regulatory mapping example
-# - Regulation processing and analysis
-# - Regulatory entity extraction and classification
-# - Regulation tracking and monitoring
-# - Example usage and documentation
-# - Performance optimization tips
-# - Advanced regulatory features

cookbook/use_cases/research/__init__.py

@@ -1,6 +0,0 @@
-"""
-Research use cases for Semantica framework.
-
-This module contains research-specific examples and use cases
-for the Semantica framework.
-"""

cookbook/use_cases/research/citation_network.py

@@ -1,14 +0,0 @@
-"""
-Citation network example for Semantica framework.
-
-This example demonstrates citation network building
-using the Semantica framework.
-"""
-
-# TODO: Implement citation network example
-# - Citation network construction and analysis
-# - Research entity extraction and classification
-# - Network analysis and insights
-# - Example usage and documentation
-# - Performance optimization tips
-# - Advanced citation features

cookbook/use_cases/research/collaboration_graph.py

@@ -1,14 +0,0 @@
-"""
-Collaboration graph example for Semantica framework.
-
-This example demonstrates researcher collaboration analysis
-using the Semantica framework.
-"""
-
-# TODO: Implement collaboration graph example
-# - Researcher collaboration analysis
-# - Collaboration entity extraction and classification
-# - Network analysis and insights
-# - Example usage and documentation
-# - Performance optimization tips
-# - Advanced collaboration features

cookbook/use_cases/research/research_trends.py

@@ -1,14 +0,0 @@
-"""
-Research trends example for Semantica framework.
-
-This example demonstrates research trend analysis
-using the Semantica framework.
-"""
-
-# TODO: Implement research trends example
-# - Research trend analysis and insights
-# - Trend entity extraction and classification
-# - Trend monitoring and reporting
-# - Example usage and documentation
-# - Performance optimization tips
-# - Advanced trend analysis features