""" 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