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feat: Implement comprehensive Semantica framework structure
- Add complete libs/semantica framework with 20+ production-ready modules - Implement core orchestration, configuration, and plugin management - Add comprehensive data ingestion (files, web, streams, databases, emails) - Implement parsing for documents, web content, structured data, code, media - Add data normalization (text, entities, dates, numbers, quality) - Implement semantic extraction (NER, relations, events, coreference, triples) - Add ontology management and knowledge graph construction - Implement graph analytics (centrality, community detection, connectivity) - Add embeddings generation and vector store management - Implement pipeline orchestration and streaming processing - Add security (access control, data masking, PII redaction) - Implement quality assurance and validation systems - Add export capabilities (RDF, JSON, CSV, graph formats) - Create comprehensive cookbook with examples and use cases - Add basic examples (document processing, web scraping, knowledge graphs) - Add advanced examples (multi-modal processing, real-time analytics) - Implement detailed bullet-point comments throughout - Follow SDK best practices and Python-only implementation - Add comprehensive pyproject.toml with dependencies and configuration - Include detailed README with usage examples and documentation This commit establishes the complete foundation for the Semantica semantic layer and knowledge engineering framework.
This commit is contained in:
KaifAhmad1
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"""
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Semantica Cookbook
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This module contains comprehensive examples and use cases for the Semantica framework.
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The cookbook is organized into different categories:
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- Basic Examples: Simple usage examples
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- Advanced Examples: Complex use cases and workflows
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- Domain Examples: Domain-specific applications
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- Integration Examples: Integration with other systems
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- Performance Examples: Performance optimization examples
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"""
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# Basic Examples
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# from .basic_examples import document_processing_example
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# from .basic_examples import web_scraping_example
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# from .basic_examples import knowledge_graph_example
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# Advanced Examples
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# from .advanced_examples import multi_modal_processing
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# from .advanced_examples import real_time_analytics
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# from .advanced_examples import federated_knowledge
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# Domain Examples
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# from .domain_examples import biomedical_knowledge
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# from .domain_examples import financial_analytics
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# from .domain_examples import legal_document_analysis
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# Integration Examples
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# from .integration_examples import database_integration
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# from .integration_examples import api_integration
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# from .integration_examples import cloud_integration
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# Performance Examples
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# from .performance_examples import large_scale_processing
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# from .performance_examples import streaming_optimization
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# from .performance_examples import memory_optimization
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"""
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Advanced Examples
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This module contains advanced usage examples for the Semantica framework.
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Examples:
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- multi_modal_processing: Process multi-modal data (text, images, audio)
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- real_time_analytics: Real-time data processing and analytics
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- federated_knowledge: Federated knowledge graph construction
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- streaming_processing: Stream processing and real-time updates
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- distributed_processing: Distributed processing across multiple nodes
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"""
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# from .multi_modal_processing import MultiModalProcessingExample
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# from .real_time_analytics import RealTimeAnalyticsExample
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# from .federated_knowledge import FederatedKnowledgeExample
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# from .streaming_processing import StreamingProcessingExample
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# from .distributed_processing import DistributedProcessingExample
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"""
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Multi-Modal Processing Example
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This example demonstrates how to process multi-modal data (text, images, audio) using Semantica.
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Key Features Demonstrated:
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- Multi-modal data ingestion
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- Cross-modal embedding generation
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- Multi-modal entity extraction
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- Cross-modal relationship detection
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- Multi-modal knowledge graph construction
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- Cross-modal similarity search
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Use Cases:
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- Social media content analysis
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- Multimedia document processing
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- Video content analysis
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- Audio transcription and analysis
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- Cross-modal information retrieval
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"""
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class MultiModalProcessingExample:
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"""
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Multi-modal processing example implementation.
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This example shows how to:
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• Process data from multiple modalities
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• Generate cross-modal embeddings
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• Extract entities from different modalities
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• Detect relationships across modalities
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• Build multi-modal knowledge graphs
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• Perform cross-modal similarity search
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"""
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def __init__(self):
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"""
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Initialize multi-modal processing example.
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• Setup Semantica framework
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• Configure multi-modal processors
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• Initialize cross-modal embedding generation
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• Setup multi-modal entity extraction
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• Configure cross-modal relationship detection
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• Setup multi-modal knowledge graph building
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"""
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# TODO: Initialize Semantica framework
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# TODO: Setup multi-modal data processors
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# TODO: Configure cross-modal embedding generation
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# TODO: Initialize multi-modal entity extraction
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# TODO: Setup cross-modal relationship detection
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# TODO: Configure multi-modal knowledge graph building
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pass
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def process_multimodal_data(self, data_sources, **options):
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"""
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Process multi-modal data from various sources.
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• Ingest data from multiple modalities
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• Process each modality appropriately
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• Generate cross-modal embeddings
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• Extract entities from all modalities
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• Detect relationships across modalities
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• Build multi-modal knowledge graph
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Args:
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data_sources: Dictionary of data sources by modality
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**options: Multi-modal processing options
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Returns:
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dict: Multi-modal processing results
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"""
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# TODO: Ingest data from multiple modalities
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# TODO: Process each modality using appropriate processors
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# TODO: Generate cross-modal embeddings using MultiModalEmbedder
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# TODO: Extract entities from all modalities
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# TODO: Detect relationships across modalities
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# TODO: Build multi-modal knowledge graph
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# TODO: Return multi-modal processing results
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pass
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def process_text_and_images(self, text_data, image_data, **options):
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"""
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Process text and image data together.
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• Process text content
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• Process image content
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• Generate cross-modal embeddings
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• Extract entities from both modalities
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• Detect relationships between text and images
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• Build cross-modal knowledge graph
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Args:
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text_data: Text data to process
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image_data: Image data to process
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**options: Text-image processing options
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Returns:
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dict: Text-image processing results
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"""
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# TODO: Process text content using TextEmbedder
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# TODO: Process image content using ImageEmbedder
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# TODO: Generate cross-modal embeddings
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# TODO: Extract entities from text and images
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# TODO: Detect relationships between text and images
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# TODO: Build cross-modal knowledge graph
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# TODO: Return text-image processing results
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pass
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def process_audio_and_text(self, audio_data, text_data, **options):
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"""
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Process audio and text data together.
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• Process audio content
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• Process text content
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• Generate cross-modal embeddings
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• Extract entities from both modalities
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• Detect relationships between audio and text
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• Build cross-modal knowledge graph
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Args:
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audio_data: Audio data to process
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text_data: Text data to process
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**options: Audio-text processing options
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Returns:
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dict: Audio-text processing results
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"""
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# TODO: Process audio content using AudioEmbedder
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# TODO: Process text content using TextEmbedder
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# TODO: Generate cross-modal embeddings
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# TODO: Extract entities from audio and text
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# TODO: Detect relationships between audio and text
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# TODO: Build cross-modal knowledge graph
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# TODO: Return audio-text processing results
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pass
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def generate_cross_modal_embeddings(self, multimodal_data, **options):
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"""
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Generate cross-modal embeddings.
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• Process data from multiple modalities
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• Generate embeddings for each modality
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• Align embeddings across modalities
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• Fuse embeddings from different modalities
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• Return cross-modal embeddings
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Args:
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multimodal_data: Multi-modal data to process
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**options: Cross-modal embedding options
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Returns:
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dict: Cross-modal embedding results
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"""
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# TODO: Use MultiModalEmbedder to generate embeddings
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# TODO: Align embeddings across modalities
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# TODO: Fuse embeddings from different modalities
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# TODO: Return cross-modal embeddings
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pass
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def extract_cross_modal_entities(self, multimodal_data, **options):
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"""
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Extract entities from multiple modalities.
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• Process each modality for entity extraction
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• Align entities across modalities
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• Resolve cross-modal entity conflicts
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• Return cross-modal entity extraction results
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Args:
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multimodal_data: Multi-modal data to process
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**options: Cross-modal entity extraction options
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Returns:
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dict: Cross-modal entity extraction results
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"""
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# TODO: Extract entities from each modality
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# TODO: Align entities across modalities
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# TODO: Resolve cross-modal entity conflicts
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# TODO: Return cross-modal entity extraction results
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pass
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def detect_cross_modal_relationships(self, multimodal_data, entities, **options):
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"""
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Detect relationships across modalities.
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• Process each modality for relationship extraction
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• Detect relationships within modalities
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• Detect relationships across modalities
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• Validate cross-modal relationships
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• Return cross-modal relationship detection results
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Args:
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multimodal_data: Multi-modal data to process
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entities: Extracted entities
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**options: Cross-modal relationship detection options
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Returns:
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dict: Cross-modal relationship detection results
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"""
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# TODO: Extract relationships within each modality
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# TODO: Detect relationships across modalities
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# TODO: Validate cross-modal relationships
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# TODO: Return cross-modal relationship detection results
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pass
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def build_multimodal_knowledge_graph(self, multimodal_data, entities, relationships, **options):
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"""
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Build multi-modal knowledge graph.
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• Integrate entities from all modalities
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• Integrate relationships from all modalities
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• Resolve cross-modal conflicts
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• Build unified knowledge graph
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• Return multi-modal knowledge graph
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Args:
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multimodal_data: Multi-modal data
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entities: Extracted entities
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relationships: Extracted relationships
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**options: Multi-modal knowledge graph building options
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Returns:
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dict: Multi-modal knowledge graph building results
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"""
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# TODO: Integrate entities from all modalities
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# TODO: Integrate relationships from all modalities
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# TODO: Resolve cross-modal conflicts
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# TODO: Build unified knowledge graph
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# TODO: Return multi-modal knowledge graph building results
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pass
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def perform_cross_modal_similarity_search(self, query, multimodal_data, **options):
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"""
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Perform cross-modal similarity search.
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• Process query across modalities
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• Generate cross-modal embeddings
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• Perform similarity search
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• Return cross-modal search results
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Args:
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query: Search query
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multimodal_data: Multi-modal data to search
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**options: Cross-modal search options
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Returns:
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dict: Cross-modal search results
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"""
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# TODO: Process query across modalities
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# TODO: Generate cross-modal embeddings
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# TODO: Perform similarity search
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# TODO: Return cross-modal search results
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pass
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def run_multimodal_processing_example():
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"""
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Run the multi-modal processing example.
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This function demonstrates the complete multi-modal processing workflow.
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"""
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# TODO: Create MultiModalProcessingExample instance
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# TODO: Define multi-modal data sources
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# TODO: Process multi-modal data
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# TODO: Extract cross-modal entities and relationships
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# TODO: Build multi-modal knowledge graph
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# TODO: Export results
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# TODO: Display results
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pass
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def run_text_image_processing_example():
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"""
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Run text-image processing example.
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This function demonstrates text-image cross-modal processing.
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"""
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# TODO: Create MultiModalProcessingExample instance
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# TODO: Define text and image data
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# TODO: Process text and image data
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# TODO: Extract cross-modal entities and relationships
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# TODO: Build cross-modal knowledge graph
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# TODO: Export results
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# TODO: Display results
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pass
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def run_audio_text_processing_example():
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"""
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Run audio-text processing example.
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This function demonstrates audio-text cross-modal processing.
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"""
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# TODO: Create MultiModalProcessingExample instance
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# TODO: Define audio and text data
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# TODO: Process audio and text data
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# TODO: Extract cross-modal entities and relationships
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# TODO: Build cross-modal knowledge graph
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# TODO: Export results
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# TODO: Display results
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pass
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"""
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Basic Examples
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This module contains basic usage examples for the Semantica framework.
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Examples:
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- document_processing_example: Process documents and extract knowledge
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- web_scraping_example: Scrape web content and build knowledge base
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- knowledge_graph_example: Build and query knowledge graphs
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- entity_extraction_example: Extract entities from text
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- relationship_extraction_example: Extract relationships between entities
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"""
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# from .document_processing_example import DocumentProcessingExample
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# from .web_scraping_example import WebScrapingExample
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# from .knowledge_graph_example import KnowledgeGraphExample
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# from .entity_extraction_example import EntityExtractionExample
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# from .relationship_extraction_example import RelationshipExtractionExample
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"""
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Document Processing Example
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This example demonstrates how to process documents and extract knowledge using Semantica.
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Key Features Demonstrated:
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- Document ingestion from various sources
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- Document parsing and content extraction
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- Text normalization and cleaning
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- Entity extraction and relationship detection
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- Knowledge graph construction
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- Export to various formats
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Use Cases:
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- Academic paper processing
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- Legal document analysis
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- Technical documentation processing
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- Research paper knowledge extraction
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"""
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class DocumentProcessingExample:
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"""
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Document processing example implementation.
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This example shows how to:
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• Ingest documents from various sources
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• Parse and extract content from different formats
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• Normalize and clean text content
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• Extract entities and relationships
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• Build knowledge graphs
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• Export results to various formats
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"""
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def __init__(self):
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"""
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Initialize document processing example.
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• Setup Semantica framework
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• Configure document processing pipeline
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• Initialize knowledge extraction tools
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• Setup export handlers
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"""
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# TODO: Initialize Semantica framework
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# TODO: Setup document processing pipeline
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# TODO: Configure entity extraction
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# TODO: Setup relationship detection
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# TODO: Initialize knowledge graph builder
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pass
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def process_documents(self, document_paths, **options):
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"""
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Process documents and extract knowledge.
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• Ingest documents from specified paths
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• Parse documents and extract content
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• Normalize and clean text content
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• Extract entities and relationships
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• Build knowledge graph
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• Export results to various formats
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Args:
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document_paths: List of document file paths
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**options: Additional processing options
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Returns:
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dict: Processing results including knowledge graph and metrics
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"""
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# TODO: Ingest documents using FileIngestor
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# TODO: Parse documents using DocumentParser
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# TODO: Normalize text using TextNormalizer
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# TODO: Extract entities using NamedEntityRecognizer
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# TODO: Extract relationships using RelationExtractor
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# TODO: Build knowledge graph using KnowledgeGraphBuilder
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# TODO: Export results using various exporters
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# TODO: Return processing results
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pass
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def process_pdf_documents(self, pdf_paths, **options):
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"""
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Process PDF documents specifically.
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• Handle PDF-specific parsing
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• Extract text and metadata
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• Process embedded content
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• Handle multi-page documents
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Args:
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pdf_paths: List of PDF file paths
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**options: PDF processing options
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Returns:
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dict: PDF processing results
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"""
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# TODO: Use PDFParser for PDF-specific processing
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# TODO: Extract text content and metadata
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# TODO: Handle embedded images and tables
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# TODO: Process multi-page documents
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# TODO: Return PDF processing results
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pass
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def process_word_documents(self, docx_paths, **options):
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"""
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Process Word documents specifically.
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• Handle DOCX-specific parsing
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• Extract text and formatting
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• Process embedded objects
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• Handle document structure
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Args:
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docx_paths: List of DOCX file paths
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**options: DOCX processing options
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Returns:
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dict: DOCX processing results
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"""
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# TODO: Use DOCXParser for DOCX-specific processing
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# TODO: Extract text content and formatting
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# TODO: Handle embedded objects and tables
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# TODO: Process document structure
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# TODO: Return DOCX processing results
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pass
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def extract_knowledge(self, processed_documents, **options):
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"""
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Extract knowledge from processed documents.
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• Extract entities and relationships
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• Build knowledge graph
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||||
• Identify knowledge patterns
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||||
• Generate knowledge insights
|
||||
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Args:
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processed_documents: Processed document data
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**options: Knowledge extraction options
|
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Returns:
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dict: Knowledge extraction results
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"""
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# TODO: Extract entities using NamedEntityRecognizer
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# TODO: Extract relationships using RelationExtractor
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# TODO: Build knowledge graph using KnowledgeGraphBuilder
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# TODO: Identify knowledge patterns
|
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# TODO: Generate knowledge insights
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# TODO: Return knowledge extraction results
|
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pass
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def export_results(self, knowledge_graph, export_formats, **options):
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"""
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Export knowledge graph to various formats.
|
||||
|
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• Export to RDF formats
|
||||
• Export to JSON formats
|
||||
• Export to CSV formats
|
||||
• Generate reports
|
||||
|
||||
Args:
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||||
knowledge_graph: Knowledge graph to export
|
||||
export_formats: List of export formats
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||||
**options: Export options
|
||||
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||||
Returns:
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||||
dict: Export results
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||||
"""
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||||
# 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
|
||||
@@ -0,0 +1,295 @@
|
||||
"""
|
||||
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
|
||||
@@ -0,0 +1,266 @@
|
||||
"""
|
||||
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
|
||||
Reference in New Issue
Block a user