semantica/README.md

🧠 Semantica

🚀 Open Source Semantic Layer and Knowledge Engineering Toolkit

Transform any unstructured data format into intelligent, structured semantic knowledge graphs, embeddings, and ontologies for LLMs, Agents, RAG systems, and Knowledge Graphs.

📖 Documentation • 🚀 Quick Start • 💡 Features • 🤝 Community • 🔧 API Reference

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🌟 What is Semantica?

Semantica is the most comprehensive semantic data transformation platform that bridges the gap between raw unstructured data in any format and intelligent AI systems. From complex documents to live data feeds, Semantica extracts meaning, builds knowledge, and creates intelligent semantic layers that power next-generation AI applications.

"The missing link between your data and AI — turning unstructured chaos into structured, intelligent semantic knowledge."

🎯 Why Choose Semantica?

📄 Universal Data Processing 50+ file formats supported Live data feeds & streaming Complex document structures Multi-modal content extraction	🧠 Advanced Semantic AI Multi-layer semantic understanding Automatic ontology generation Triple extraction & knowledge graphs Context-aware embeddings
🤖 AI-Ready Outputs RAG-optimized chunking LLM-compatible schemas Vector embeddings Agent orchestration	🚀 Enterprise Scale Real-time processing Distributed architecture 99.9% uptime SLA SOC2/GDPR compliant

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✨ Features

📊 Supported Data Formats

Category	Formats	Count
Document Formats	PDF, DOCX, XLSX, PPTX, TXT, RTF, ODT, EPUB, LaTeX	9
Web Formats	HTML, XML, XHTML, RSS, Atom, JSON-LD, Sitemap XML	7
Structured Data	JSON, YAML, CSV, TSV, Parquet, Avro, ORC	7
Markup Languages	Markdown, ReStructuredText, AsciiDoc, Confluence	4
Feed Processing	RSS/Atom feeds, news feeds, social media streams	3
Email Formats	EML, MSG, MBOX, PST archives	4
Archive Formats	ZIP, TAR, RAR, 7Z with recursive processing	4
Database Exports	SQL dumps, MongoDB exports, CouchDB	3
Scientific Formats	BibTeX, EndNote, RIS, JATS XML	4
Code Repositories	Git repositories, documentation, README files	3
Total Supported Formats		50+

🧠 Semantic Processing Capabilities

Feature	Description	Supported Models
Multi-layer Understanding	Lexical, syntactic, semantic, and pragmatic analysis	Custom NLP pipelines
Entity & Relationship Extraction	Named entities, relationships, and complex event detection	spaCy, NLTK, Custom
Automatic Triple Generation	Subject-Predicate-Object triples from any content	RDF, JSON-LD, Custom
Context Preservation	Maintain semantic context across document boundaries	Advanced chunking
Temporal Analysis	Time-aware semantic understanding and event sequencing	Temporal reasoning
Cross-Document Linking	Entity resolution and relationship mapping across sources	Graph algorithms
Ontology Alignment	Automatic mapping to existing ontologies	Schema.org, FOAF, Dublin Core

🕸️ Knowledge Graph Features

Feature	Description	Supported Backends
Automated Construction	Build knowledge graphs from any data format	All major graph DBs
Triple Stores	Blazegraph, Virtuoso, Apache Jena, GraphDB	4+ engines
Graph Databases	Neo4j, KuzuDB, ArangoDB, Amazon Neptune, TigerGraph	5+ databases
Semantic Reasoning	Inductive, deductive, and abductive reasoning	Custom reasoning engines
Ontology Generation	Automatic OWL/RDF ontology creation from data patterns	OWL 2.0, RDF 1.1
Graph Analytics	Centrality analysis, community detection, path finding	NetworkX, Custom
SPARQL Query Generation	Automatic query generation for semantic search	SPARQL 1.1

📈 Content Transformation Features

Feature	Description	Output Formats
Semantic Chunking	Context-aware document segmentation for RAG systems	JSON, CSV, Custom
Multi-Modal Embeddings	Text, image, table, and chart embeddings	OpenAI, Cohere, Custom
Schema Evolution	Dynamic schema adaptation and versioning	JSON Schema, XSD
Content Enrichment	Automatic metadata extraction and enhancement	Dublin Core, Custom
Cross-Reference Resolution	Link resolution across documents and formats	Graph links
Summarization	Extractive and abstractive summarization with semantic preservation	Text, JSON

🔍 Text & Content Analysis

Feature	Description	Supported Languages
Topic Modeling	LDA, BERTopic, hierarchical topic discovery	100+ languages
Sentiment Analysis	Document, sentence, and aspect-level sentiment	Multi-language
Language Detection	100+ languages with confidence scoring	100+ languages
Content Classification	Automatic categorization and tagging	Custom taxonomies
Duplicate Detection	Semantic similarity and near-duplicate identification	Fuzzy matching
Information Extraction	Tables, figures, citations, references	Multi-format

🌐 Live Data Processing

Feature	Description	Supported Platforms
RSS/Atom Feed Monitoring	Real-time feed processing and semantic extraction	All RSS/Atom feeds
Web Scraping	Intelligent web content extraction with semantic understanding	Any web content
API Integration	REST, GraphQL, WebSocket real-time data processing	Standard APIs
Stream Processing	Kafka, RabbitMQ, Pulsar integration	3+ platforms
Social Media Feeds	Twitter, LinkedIn, Reddit semantic monitoring	3+ platforms
News Aggregation	Multi-source news processing and semantic analysis	Global news sources

🏢 Enterprise Features

Feature	Description	Enterprise Tier
Schema-First Construction	Predefined business schemas with validation	Pro+
Seed-Based Initialization	Start with known entities and enhance	Pro+
Duplicate Detection	Automatic deduplication with business rules	Pro+
Conflict Detection	Flag contradictions with source tracking	Pro+
Business Rules Engine	Custom business logic and constraints	Enterprise
Interactive Dashboard	Built-in UI for conflict resolution	Enterprise

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🚀 Quick Start

📦 Installation Options

# Complete installation with all format support
pip install "semantica[all]"

# Lightweight installation
pip install semantica

# Specific format support
pip install "semantica[pdf,web,feeds,office]"

# Development installation
git clone https://github.com/semantica/semantica.git
cd semantica
pip install -e ".[dev]"

⚡ 30-Second Demo: From Any Format to Knowledge

from semantica import Semantica

# Initialize with preferred providers
core = Semantica(
    llm_provider="openai",
    embedding_model="text-embedding-3-large",
    vector_store="pinecone",
    graph_db="neo4j"
)

# Process ANY data format
sources = [
    "financial_report.pdf",
    "https://example.com/news/rss",
    "research_papers/",
    "data.json",
    "https://example.com/article"
]

# One-line semantic transformation
knowledge_base = core.build_knowledge_base(sources)

print(f"Processed {len(knowledge_base.documents)} documents")
print(f"Extracted {len(knowledge_base.entities)} entities")
print(f"Generated {len(knowledge_base.triples)} semantic triples")
print(f"Created {len(knowledge_base.embeddings)} vector embeddings")

# Query the knowledge base
results = knowledge_base.query("What are the key financial trends?")

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🔧 Core Modules

📄 Document Processing Module

• Supported Formats: PDF, DOCX, XLSX, PPTX, TXT, RTF, ODT, EPUB, LaTeX
• Features: Table extraction, image processing, metadata extraction, structure preservation
• Use Cases: Document analysis, content extraction, structured data conversion

🌐 Web & Feed Processing Module

• Supported Formats: HTML, XML, RSS, Atom, JSON-LD, Sitemaps
• Features: Real-time monitoring, content extraction, metadata parsing
• Use Cases: Web scraping, feed aggregation, content monitoring

📊 Structured Data Processing Module

• Supported Formats: JSON, YAML, CSV, TSV, Parquet, Avro, ORC
• Features: Schema inference, relationship extraction, ontology generation
• Use Cases: Data integration, schema mapping, knowledge extraction

📧 Email & Archive Processing Module

• Supported Formats: EML, MSG, MBOX, PST, ZIP, TAR, RAR, 7Z
• Features: Attachment processing, thread detection, recursive extraction
• Use Cases: Email analysis, archive processing, content discovery

🔬 Scientific & Academic Processing Module

• Supported Formats: LaTeX, BibTeX, EndNote, RIS, JATS XML
• Features: Citation extraction, reference parsing, figure identification
• Use Cases: Research analysis, academic content processing, literature review

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🎯 Advanced Use Cases

🔐 Multi-Format Cybersecurity Intelligence

• Data Sources: Threat reports, security blogs, vulnerability databases, incident reports
• Outputs: STIX bundles, MISP integration, OpenCTI export
• Features: IOC extraction, MITRE ATT&CK mapping, threat intelligence

🧬 Biomedical Literature Processing

• Data Sources: Research papers, PubMed feeds, clinical reports, drug databases
• Outputs: Medical ontologies, UMLS integration, BioPortal export
• Features: Drug interaction detection, MeSH mapping, clinical trial analysis

📊 Financial Data Aggregation & Analysis

• Data Sources: SEC filings, financial news, market data, earnings reports
• Outputs: Financial knowledge graphs, Bloomberg API, Refinitiv export
• Features: News sentiment analysis, regulatory compliance, market intelligence

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🏗️ Enterprise Architecture

🚀 Scalable Deployment Options

• Kubernetes: Auto-scaling, resource management, high availability
• Docker: Containerized deployment, easy scaling, portability
• Cloud Native: AWS, Azure, GCP integration with managed services
• On-Premise: Self-hosted solutions with enterprise security

🔧 Custom Pipeline Configuration

• Modular Design: Mix and match processing components
• Custom Rules: Business logic and validation engines
• Quality Control: Built-in validation and conflict detection
• Monitoring: Real-time analytics and performance dashboards

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📈 Performance & Monitoring

📊 Real-Time Analytics Dashboard

• Metrics: Processing rate, extraction accuracy, memory usage, knowledge graph growth
• Alerts: Configurable thresholds and notifications
• Visualization: Interactive charts and performance graphs
• Integration: Slack, email, webhook notifications

🔍 Quality Assurance & Validation

• Validation Rules: Entity consistency, triple validity, schema compliance
• Confidence Scoring: Configurable thresholds for extraction quality
• Continuous Monitoring: Real-time quality assessment
• Issue Resolution: Automated problem detection and resolution

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🤝 Community & Support

🎓 Learning Resources

• 📚 Documentation - Comprehensive guides and API reference
• 🎯 Tutorials - Step-by-step tutorials for common use cases
• 💡 Examples Repository - Real-world implementation examples
• 🎥 Video Tutorials - Visual learning content
• 📖 Blog - Latest updates and best practices

💬 Community Support

• 💬 Discord Community - Real-time chat and support
• 🐙 GitHub Discussions - Community Q&A
• 📧 Mailing List - Announcements and updates
• 🐦 Twitter - Latest news and tips

🏢 Enterprise Support

• 🎯 Professional Services - Custom implementation and consulting
• 📞 24/7 Support - Enterprise-grade support with SLA
• 🏫 Training Programs - On-site and remote training for teams
• 🔒 Security Audits - Comprehensive security assessments

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🙏 Acknowledgments

• 🧠 Research Community - Built upon cutting-edge research in NLP and semantic web
• 🤝 Open Source Contributors - Hundreds of contributors making Semantica better
• 🏢 Enterprise Partners - Real-world feedback and requirements shaping development
• 🎓 Academic Institutions - Research collaborations and validation

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🚀 Ready to transform your data into intelligent knowledge?

Get Started Now • View Examples • Join Community