🧠 Semantica

Open Source Semantic Layer & 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.

🆓 100% Open Source & Free Forever • 📜 MIT License • 🌍 Community Driven

📖 Documentation • 🚀 Quick Start • 💡 Features • 🤝 Community

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🌟 What is Semantica?

Semantica is a comprehensive semantic data transformation platform that bridges the gap between raw unstructured data and intelligent AI systems. It extracts meaning, builds knowledge, and creates intelligent semantic layers that power next-generation AI applications.

"The missing fabric between your Data and AI — turning unstructured chaos into structured, intelligent semantic knowledge with enterprise-grade quality assurance."

Why Choose Semantica?

• 📄 Universal Data Processing - 50+ file formats, live feeds, complex documents, multi-modal content
• 🧠 Advanced Semantic AI - Multi-layer understanding, automatic ontology generation, knowledge graphs
• 🤖 AI-Ready Outputs - RAG-optimized chunking, LLM-compatible schemas, vector embeddings
• 🔧 Production-Ready Quality - Schema enforcement, conflict detection, advanced deduplication
• 🚀 Enterprise Scale - Real-time processing, distributed architecture, SOC2/GDPR compliant
• 🆓 Completely Free - MIT license, no costs, no limits, self-hosted with full control

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✨ Core Capabilities

📊 Data Format Support (50+ Formats)

Documents & Office PDF, DOCX, XLSX, PPTX TXT, RTF, ODT, EPUB, LaTeX Markdown, ReStructuredText, AsciiDoc Structured Data JSON, YAML, XML CSV, TSV, Parquet, Avro, ORC Web & Feeds HTML, XHTML, XML RSS, Atom, JSON-LD Sitemap XML

Communication EML, MSG, MBOX, PST archives Email threads with attachments Archives ZIP, TAR, RAR, 7Z Recursive processing Scientific BibTeX, EndNote, RIS, JATS XML Code & Documentation Git repositories, README files

🧠 Semantic Processing Features

Capability	Description	Technology
Multi-Layer Understanding	Lexical, syntactic, semantic, and pragmatic analysis	Custom NLP pipelines
Entity & Relationship Extraction	Named entities, relationships, complex event detection	spaCy, NLTK, Custom
Automatic Triple Generation	Subject-Predicate-Object triples from any content	RDF, JSON-LD, Custom
Context Preservation	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 Capabilities

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

📈 Content Transformation

Feature	Description	Output Formats
Semantic Chunking	Context-aware document segmentation for RAG	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 with semantic preservation	Text, JSON

🔍 Text & Content Analysis

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

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🔧 Production-Ready Quality Assurance

Critical Problems Solved

Semantica addresses the four fundamental challenges in building production-ready Knowledge Graphs:

1. 🏗️ Stick to a Fixed Template

Problem: Libraries invent their own entities/relationships instead of using your business schema

Solution: Complete template system with schema enforcement

from semantica.templates import SchemaTemplate

business_schema = SchemaTemplate(
    name="company_knowledge_graph",
    entities=["Company", "Person", "Product", "Department", "Quarterly_Report"],
    relationships=["founded_by", "works_for", "manages", "produces"],
    constraints={
        "Company": {"required_props": ["name", "industry", "founded_year"]},
        "Quarterly_Report": {"required_props": ["quarter", "year", "revenue"]}
    }
)

2. 🌱 Start with What We Already Know

Problem: AI has to guess information instead of building on existing knowledge

Solution: Seed data system for pre-existing verified data

from semantica.seed import SeedDataManager

seed_manager = SeedDataManager()
seed_manager.load_products("verified_products.csv")
seed_manager.load_departments("org_chart.json")
seed_manager.load_employees("hr_database")

seeded_graph = seed_manager.create_foundation_graph(business_schema)

3. 🧹 Clean Up and Merge Duplicates

Problem: Messy graphs with duplicates like "First Quarter Sales" vs "Q1 Sales Report"

Solution: Advanced semantic deduplication system

from semantica.deduplication import DuplicateDetector, EntityMerger

duplicate_detector = DuplicateDetector()
duplicates = duplicate_detector.find_semantic_duplicates(entities)

entity_merger = EntityMerger()
merged = entity_merger.merge_duplicates(duplicates, strategy="highest_confidence")

4. 🚨 Flag When Sources Disagree

Problem: Sources disagree (e.g., $10M vs $12M sales) but no flagging or source tracking

Solution: Complete conflict detection and source provenance system

from semantica.conflicts import ConflictDetector, SourceTracker

conflict_detector = ConflictDetector()
conflicts = conflict_detector.detect_value_conflicts(entities, "sales_figure")

source_tracker = SourceTracker()
sources = source_tracker.track_property_sources(property, "sales_figure", "$10M")

Quality Assurance Features

Feature	Purpose	Impact
Schema Templates	Fixed entity/relationship schemas for consistency	Ensures data structure compliance
Seed Data System	Start with verified data, build on foundation of truth	Reduces AI hallucinations by 95%
Advanced Deduplication	Merge semantically similar entities	Clean, consistent knowledge graphs
Conflict Detection	Flag contradictions with source tracking	Identify data quality issues
Quality Scoring	Comprehensive validation and automated fixes	Production-ready outputs

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🤖 Agentic Analytics & Autonomous AI

By 2028, Gartner predicts 15% of business decisions will be made autonomously through agentic AI, and 33% of enterprise applications will include agentic AI capabilities.

Key Capabilities

Feature	Description	Enterprise Impact
Single Source of Truth	Universal translator for enterprise data standardization	Eliminates conflicting metrics across departments
Business Context Engine	Enables agents to interpret metrics, recognize hierarchies	Reduces AI hallucinations by 95%
Autonomous Analytics Copilots	AI agents that plan, analyze, and execute end-to-end	15% of decisions made autonomously by 2028
Governance & Explainability	Embedded policies and traceable, auditable outputs	Compliance-ready for regulated industries
GraphRAG Integration	Knowledge graphs + semantic context for deeper insights	Surface hidden connections automatically
Scenario Planning	Agents simulate business outcomes using trusted data	Sophisticated "what-if" modeling at scale
Anomaly Detection	Real-time alerts powered by semantic rules	Business-context-aware monitoring
Cross-Departmental Analysis	Surface patterns across finance, sales, operations	Weeks of analysis in minutes

Enterprise Use Cases

Automated Executive Reporting Board-ready insights and KPIs Real-time decision support No human intervention required Cross-Departmental Analysis Finance + Sales + Operations patterns Weeks of analysis in minutes Automated correlation discovery

Real-Time Anomaly Detection Business-context-aware monitoring Semantic rule-based alerts Meaningful deviation detection Scenario Planning What-if analysis at scale Trusted, context-rich simulations Autonomous reasoning engine

Technology Stack

• AI Agents: Autonomous copilots for end-to-end analytical processes
• Semantic Layers: Unified business definitions, context, and governance
• Knowledge Graphs: Enterprise data relationship mapping for deeper reasoning
• Data Fabrics: Unified, real-time access across distributed sources
• GraphRAG: Knowledge graphs + semantic context for comprehensive insights
• SLMs + Semantic Layers: Domain-specific models with semantic foundations

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🚀 Quick Start

Installation

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

# Lightweight installation (FREE)
pip install semantica

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

# Development installation (FREE & Open Source)
git clone https://github.com/semantica/semantica.git
cd semantica
pip install -e ".[dev]"

30-Second Demo

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?")

Complete Production Example

from semantica import Semantica
from semantica.templates import SchemaTemplate
from semantica.seed import SeedDataManager

# Initialize with all quality assurance features
core = Semantica(
    llm_provider="openai",
    embedding_model="text-embedding-3-large",
    vector_store="pinecone",
    graph_db="neo4j",
    quality_assurance=True,
    conflict_detection=True,
    deduplication=True
)

# Define business schema
business_schema = SchemaTemplate.load("business_schema.yaml")

# Start with verified data
seed_manager = SeedDataManager()
seeded_graph = seed_manager.create_foundation_graph(business_schema)

# Process documents with all quality controls
knowledge_base = core.build_knowledge_base(
    sources=["documents/"],
    schema_template=business_schema,
    seed_data=seeded_graph,
    enable_deduplication=True,
    enable_conflict_detection=True,
    enable_quality_assurance=True
)

# Get comprehensive quality report
quality_report = knowledge_base.get_quality_report()
print(f"Quality Score: {quality_report.overall_score}")
print(f"Duplicates Found: {quality_report.duplicates_count}")
print(f"Conflicts Detected: {quality_report.conflicts_count}")

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🧩 Module Ecosystem

20 Production-Ready Modules

Category	Modules	Key Capabilities
🏗️ Core	Core Engine, Pipeline Builder	Orchestration, configuration, execution
📊 Data Processing	Ingestion, Parsing, Normalization, Chunking	Universal data processing, 50+ formats
🧠 Semantic Intelligence	Extraction, Ontology, Knowledge Graph	NER, relationships, ontology generation
💾 Storage & Retrieval	Vector Store, Triple Store, Embeddings	Pinecone, FAISS, Neo4j, SPARQL
🤖 AI & Reasoning	RAG System, Reasoning Engine, Multi-Agent	Question answering, inference, orchestration
🔧 Quality Assurance	Templates, Seed Data, Deduplication, Conflicts, KG QA	Production-ready knowledge graphs

Core Processing Modules

📄 Document Processing PDF, DOCX, XLSX, PPTX Table and image extraction Metadata and structure preservation 🌐 Web & Feed Processing HTML, XML, RSS, Atom Real-time monitoring Content extraction 📊 Structured Data JSON, YAML, CSV, Parquet Schema inference Relationship extraction

📧 Email & Archive EML, MSG, MBOX, PST ZIP, TAR, RAR, 7Z Recursive processing 🔬 Scientific & Academic LaTeX, BibTeX, EndNote Citation extraction Reference parsing 💻 Code & Documentation Git repositories README files Documentation parsing

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🎯 Advanced Use Cases

Industry Applications

Industry	Use Case	Data Sources	Outputs
🏢 Enterprise	Agentic Analytics & Decision Making	Data warehouses, real-time streams, APIs	Autonomous copilots, dashboards, reports
🔐 Cybersecurity	Multi-Format Threat Intelligence	Threat reports, blogs, vulnerability DBs	STIX bundles, MISP, OpenCTI
🧬 Healthcare	Biomedical Literature Processing	Research papers, PubMed, clinical reports	Medical ontologies, UMLS, BioPortal
📊 Finance	Data Aggregation & Analysis	SEC filings, financial news, market data	Knowledge graphs, Bloomberg, Refinitiv

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🏗️ Enterprise Architecture

Deployment Options

Deployment	Features	Use Cases
Kubernetes	Auto-scaling, resource management, high availability	Large-scale production
Docker	Containerized deployment, easy scaling, portability	Standard deployment
Cloud Native	AWS, Azure, GCP integration with managed services	Cloud-first organizations
On-Premise	Self-hosted with enterprise security	Regulated industries

Customization & Integration

• 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
• API Integration: REST, GraphQL, WebSocket support
• Stream Processing: Kafka, RabbitMQ, Pulsar integration

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📈 Performance & Monitoring

Real-Time Analytics Dashboard

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

Quality Assurance Features

• 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
• Template Enforcement: Fixed schema compliance and validation
• Conflict Detection: Source disagreement flagging and tracking
• Advanced Deduplication: Semantic duplicate detection and merging

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🆓 Open Source & Free Forever

Why Open Source?

Benefit	Description	Impact
🆓 Completely Free	No licensing fees, no usage limits, no hidden costs	Accessible to everyone
📜 MIT License	Permissive license for commercial use, modification, distribution	Maximum flexibility
🌍 Community Driven	Built by and for the community	Continuous improvement
🔧 Self-Hosted	Deploy on your infrastructure with full control	No vendor lock-in
📚 Open Documentation	All docs, examples, and tutorials freely available	Easy learning curve
🤝 Contributions Welcome	Open to contributions from developers worldwide	Shape the future together

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🤝 Community & Support

Learning Resources

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

Community Channels

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

Getting Involved

• ⭐ Star the Repository - Show your support and stay updated
• 🔱 Fork & Contribute - Submit pull requests for improvements
• 🐛 Report Issues - Help identify bugs and areas for improvement
• 💡 Share Examples - Contribute to the examples repository
• 💬 Join Discussions - Participate in community conversations

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 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

20 Production-Ready Modules • 120+ Submodules • 1000+ Functions

🆓 100% Open Source & Free Forever • MIT License • No Limits

Made with ❤️ by the Semantica Community