mirror of
https://github.com/semantica-agi/semantica.git
synced 2026-08-29 04:26:20 +00:00
KaifAhmad1
aa10b9434c
- 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.
296 lines
9.4 KiB
Python
296 lines
9.4 KiB
Python
"""
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Knowledge Graph Example
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This example demonstrates how to build and query knowledge graphs using Semantica.
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Key Features Demonstrated:
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- Knowledge graph construction
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- Entity resolution and deduplication
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- Relationship extraction and validation
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- Graph analytics and centrality measures
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- Knowledge graph querying
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- Graph visualization and export
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Use Cases:
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- Enterprise knowledge management
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- Research knowledge organization
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- Domain-specific knowledge graphs
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- Knowledge discovery and exploration
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- Knowledge graph analytics
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"""
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class KnowledgeGraphExample:
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"""
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Knowledge graph example implementation.
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This example shows how to:
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• Build knowledge graphs from various data sources
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• Resolve entities and handle deduplication
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• Extract and validate relationships
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• Perform graph analytics and centrality analysis
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• Query knowledge graphs
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• Export and visualize graphs
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"""
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def __init__(self):
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"""
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Initialize knowledge graph example.
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• Setup Semantica framework
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• Configure knowledge graph builder
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• Initialize entity resolution
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• Setup relationship extraction
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• Configure graph analytics
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• Setup query engines
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"""
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# TODO: Initialize Semantica framework
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# TODO: Setup KnowledgeGraphBuilder
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# TODO: Configure EntityResolver
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# TODO: Setup RelationExtractor
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# TODO: Initialize GraphAnalyzer
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# TODO: Setup query engines
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pass
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def build_knowledge_graph(self, data_sources, **options):
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"""
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Build knowledge graph from data sources.
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• Process data from various sources
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• Extract entities and relationships
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• Resolve entity duplicates
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• Build knowledge graph structure
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• Validate graph consistency
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• Return knowledge graph
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Args:
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data_sources: List of data sources to process
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**options: Knowledge graph building options
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Returns:
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dict: Knowledge graph building results
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"""
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# TODO: Process data sources using appropriate ingestors
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# TODO: Extract entities using NamedEntityRecognizer
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# TODO: Extract relationships using RelationExtractor
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# TODO: Resolve entities using EntityResolver
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# TODO: Build knowledge graph using KnowledgeGraphBuilder
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# TODO: Validate graph consistency
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# TODO: Return knowledge graph
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pass
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def resolve_entities(self, entities, **options):
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"""
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Resolve entity duplicates and conflicts.
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• Identify duplicate entities
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• Resolve entity conflicts
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• Merge duplicate entities
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• Update entity references
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• Return resolved entities
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Args:
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entities: List of entities to resolve
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**options: Entity resolution options
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Returns:
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dict: Entity resolution results
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"""
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# TODO: Use EntityResolver to identify duplicates
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# TODO: Resolve entity conflicts
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# TODO: Merge duplicate entities
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# TODO: Update entity references
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# TODO: Return resolved entities
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pass
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def extract_relationships(self, entities, text_content, **options):
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"""
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Extract relationships between entities.
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• Process text content for relationships
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• Extract relationships between entities
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• Validate relationship quality
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• Classify relationship types
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• Return relationship extraction results
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Args:
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entities: List of entities
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text_content: Text content to process
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**options: Relationship extraction options
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Returns:
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dict: Relationship extraction results
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"""
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# TODO: Use RelationExtractor to extract relationships
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# TODO: Validate relationship quality
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# TODO: Classify relationship types
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# TODO: Return relationship extraction results
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pass
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def analyze_graph_centrality(self, knowledge_graph, **options):
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"""
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Analyze graph centrality measures.
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• Calculate degree centrality
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• Calculate betweenness centrality
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• Calculate closeness centrality
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• Calculate eigenvector centrality
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• Return centrality analysis results
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Args:
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knowledge_graph: Knowledge graph to analyze
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**options: Centrality analysis options
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Returns:
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dict: Centrality analysis results
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"""
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# TODO: Use GraphAnalyzer to calculate centrality measures
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# TODO: Calculate degree centrality
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# TODO: Calculate betweenness centrality
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# TODO: Calculate closeness centrality
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# TODO: Calculate eigenvector centrality
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# TODO: Return centrality analysis results
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pass
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def detect_communities(self, knowledge_graph, **options):
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"""
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Detect communities in knowledge graph.
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• Apply community detection algorithms
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• Identify community structures
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• Calculate community metrics
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• Handle overlapping communities
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• Return community detection results
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Args:
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knowledge_graph: Knowledge graph to analyze
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**options: Community detection options
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Returns:
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dict: Community detection results
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"""
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# TODO: Use GraphAnalyzer to detect communities
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# TODO: Apply community detection algorithms
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# TODO: Identify community structures
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# TODO: Calculate community metrics
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# TODO: Return community detection results
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pass
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def analyze_connectivity(self, knowledge_graph, **options):
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"""
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Analyze graph connectivity and structure.
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• Calculate connectivity metrics
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• Identify connected components
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• Analyze path lengths and distances
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• Detect bottlenecks and bridges
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• Return connectivity analysis
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Args:
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knowledge_graph: Knowledge graph to analyze
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**options: Connectivity analysis options
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Returns:
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dict: Connectivity analysis results
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"""
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# TODO: Use GraphAnalyzer to analyze connectivity
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# TODO: Calculate connectivity metrics
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# TODO: Identify connected components
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# TODO: Analyze path lengths and distances
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# TODO: Detect bottlenecks and bridges
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# TODO: Return connectivity analysis results
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pass
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def query_knowledge_graph(self, knowledge_graph, query, **options):
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"""
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Query knowledge graph using various query languages.
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• Process SPARQL queries
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• Process natural language queries
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• Process graph traversal queries
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• Return query results
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Args:
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knowledge_graph: Knowledge graph to query
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query: Query to execute
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**options: Query options
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Returns:
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dict: Query results
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"""
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# TODO: Use query engines to process queries
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# TODO: Process SPARQL queries
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# TODO: Process natural language queries
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# TODO: Process graph traversal queries
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# TODO: Return query results
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pass
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def export_knowledge_graph(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
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• Export to JSON formats
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• Export to graph formats
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• Generate visualization files
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Args:
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knowledge_graph: Knowledge graph to export
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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: Use various exporters to export knowledge graph
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# TODO: Export to RDF formats using RDFExporter
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# TODO: Export to JSON formats using JSONExporter
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# TODO: Export to graph formats using GraphExporter
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# TODO: Generate visualization files
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# TODO: Return export results
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pass
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def run_knowledge_graph_example():
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"""
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Run the knowledge graph example.
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This function demonstrates the complete knowledge graph workflow.
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"""
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# TODO: Create KnowledgeGraphExample instance
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# TODO: Define data sources
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# TODO: Build knowledge graph
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# TODO: Analyze graph properties
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# TODO: Query 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_centrality_analysis_example():
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"""
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Run centrality analysis example.
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This function demonstrates graph centrality analysis.
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"""
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# TODO: Create KnowledgeGraphExample instance
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# TODO: Build knowledge graph
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# TODO: Analyze centrality measures
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# TODO: Display centrality results
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# TODO: Export centrality analysis
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pass
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def run_community_detection_example():
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"""
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Run community detection example.
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This function demonstrates community detection in knowledge graphs.
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"""
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# TODO: Create KnowledgeGraphExample instance
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# TODO: Build knowledge graph
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# TODO: Detect communities
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# TODO: Display community results
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# TODO: Export community analysis
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pass
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