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feat: Implement comprehensive Semantica framework structure

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- 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
2025-10-22 22:47:05 +05:30
parent 149d558279
commit aa10b9434c
66 changed files with 11726 additions and 1606 deletions
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cookbook/basic_examples/knowledge_graph_example.py
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"""
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