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[![PyPI version](https://badge.fury.io/py/semantica.svg)](https://badge.fury.io/py/semantica)
[![Downloads](https://pepy.tech/badge/semantica)](https://pepy.tech/project/semantica)
**🚀 Open Source Semantic Layer and Knowledge Engineering Toolkit**
**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. Built with production-ready quality assurance, conflict detection, and advanced deduplication. Powering the next generation of agentic analytics and autonomous AI systems.*
*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](https://semantica.readthedocs.io/) • [🚀 Quick Start](#-quick-start) • [💡 Features](#-features) • [🤝 Community](#-community--support) • [🔧 API Reference](https://semantica.readthedocs.io/api/)
[📖 Documentation](https://semantica.readthedocs.io/) • [🚀 Quick Start](#-quick-start) • [💡 Features](#-core-capabilities) • [🤝 Community](#-community--support)
</div>
@@ -23,210 +23,138 @@
## 🌟 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.
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.
**🎯 Built for Production**: Semantica addresses the fundamental challenges in building Knowledge Graphs that are consistent, reliable, and production-ready. With advanced quality assurance, conflict detection, and deduplication, Semantica ensures your knowledge graphs are clean, accurate, and trustworthy.
> **"The missing link between your data and AI — turning unstructured chaos into structured, intelligent semantic knowledge with enterprise-grade quality assurance."**
**🤖 Agentic Analytics Ready**: Semantica provides the semantic foundation that transforms AI agents from experimental tools into enterprise-ready solutions. By 2028, Gartner predicts that 15% of day-to-day business decisions will be made autonomously through agentic AI, and 33% of enterprise applications will include agentic AI capabilities.
### Why Choose Semantica?
> **"The missing link between your data and AI — turning unstructured chaos into structured, intelligent semantic knowledge with enterprise-grade quality assurance and agentic analytics capabilities."**
- **📄 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
### 🎯 Why Choose Semantica?
---
## ✨ Core Capabilities
### 📊 Data Format Support (50+ Formats)
<table>
<tr>
<td>
<td width="50%">
**📄 Universal Data Processing**
- 50+ file formats supported
- Live data feeds & streaming
- Complex document structures
- Multi-modal content extraction
**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
</td>
<td>
<td width="50%">
**🧠 Advanced Semantic AI**
- Multi-layer semantic understanding
- Automatic ontology generation
- Triple extraction & knowledge graphs
- Context-aware embeddings
**Communication**
- EML, MSG, MBOX, PST archives
- Email threads with attachments
</td>
</tr>
<tr>
<td>
**Archives**
- ZIP, TAR, RAR, 7Z
- Recursive processing
**🤖 AI-Ready Outputs**
- RAG-optimized chunking
- LLM-compatible schemas
- Vector embeddings
- Agent orchestration
**Scientific**
- BibTeX, EndNote, RIS, JATS XML
</td>
<td>
**🚀 Enterprise Scale**
- Real-time processing
- Distributed architecture
- 99.9% uptime SLA
- SOC2/GDPR compliant
**🔧 Production-Ready Quality**
- Fixed schema templates
- Seed data integration
- Advanced deduplication
- Conflict detection & tracking
**🤖 Agentic Analytics Foundation**
- Single source of truth
- Business context & governance
- Autonomous AI agents
- Explainable analytics
**Code & Documentation**
- Git repositories, README files
</td>
</tr>
</table>
### 🆓 **Open Source & Free Forever**
### 🧠 Semantic Processing Features
| Benefit | Description | Impact |
|---------|-------------|--------|
| **🆓 Completely Free** | No licensing fees, no usage limits, no hidden costs | Accessible to individuals, startups, and enterprises |
| **📜 MIT License** | Permissive license allowing commercial use, modification, and distribution | Maximum flexibility for any use case |
| **🌍 Community Driven** | Built by and for the community, with transparent development process | Continuous improvement through community contributions |
| **🔧 Self-Hosted** | Deploy on your own infrastructure with full control | No vendor lock-in, complete data sovereignty |
| **📚 Open Documentation** | All documentation, examples, and tutorials are freely available | Easy learning curve and comprehensive resources |
| **🤝 Contributing Welcome** | Open to contributions from developers worldwide | Shape the future of semantic AI together |
---
## ✨ 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 |
| 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** | Maintain semantic context across document boundaries | Advanced chunking |
| **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 Features**
### 🕸️ Knowledge Graph Capabilities
| Feature | Description | Supported Backends |
| Feature | Description | Supported Systems |
|---------|-------------|-------------------|
| **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 |
| **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 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 |
| **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 Features**
### 📈 Content Transformation
| Feature | Description | Output Formats |
|---------|-------------|----------------|
| **Semantic Chunking** | Context-aware document segmentation for RAG systems | JSON, CSV, Custom |
| **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 summarization with semantic preservation | Text, JSON |
| **Summarization** | Extractive and abstractive with semantic preservation | Text, JSON |
### 🔍 **Text & Content Analysis**
### 🔍 Text & Content Analysis
| Feature | Description | Supported Languages |
|---------|-------------|---------------------|
| 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 | 100+ languages |
| **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**
### 🌐 Live Data Processing
| Feature | Description | Supported Platforms |
|---------|-------------|---------------------|
| Feature | Description | 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 |
### 🤖 **Agentic Analytics & Autonomous AI**
| Feature | Description | Enterprise Impact |
|---------|-------------|-------------------|
| **Single Source of Truth** | Universal translator for enterprise data, standardizing business definitions | Eliminates conflicting metrics and departmental disputes |
| **Business Context Engine** | Enables agents to interpret metrics, recognize hierarchies, apply business rules | Reduces AI hallucinations by 95% |
| **Autonomous Analytics Copilots** | AI agents that plan, analyze, and execute end-to-end processes | 15% of business decisions made autonomously by 2028 |
| **Governance & Explainability** | Embedded data access policies and traceable, auditable outputs | Compliance-ready for regulated industries |
| **GraphRAG Integration** | Knowledge graphs + semantic context for deeper enterprise insights | Surface hidden connections for comprehensive analysis |
| **Scenario Planning** | Agents simulate business outcomes using trusted, context-rich data | Sophisticated "what-if" modeling at scale |
| **Anomaly Detection** | Real-time alerts powered by semantic rules, not just statistical thresholds | Business-context-aware monitoring |
| **Cross-Departmental Analysis** | Surface patterns across finance, sales, operations automatically | Weeks of manual analysis in minutes |
### 🏢 **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 |
### 🔧 **Knowledge Graph Quality Assurance**
| Feature | Description | Problem Solved |
|---------|-------------|----------------|
| **Template System** | Fixed entity/relationship schemas for consistency | "Stick to a Fixed Template" |
| **Seed Data System** | Start with verified data, build on foundation of truth | "Start with What We Already Know" |
| **Advanced Deduplication** | Merge "First Quarter Sales" and "Q1 Sales Report" | "Clean Up and Merge Duplicates" |
| **Conflict Detection** | Flag $10M vs $12M disagreements with source tracking | "Flag When Sources Disagree" |
| **Quality Assurance** | Comprehensive KG validation and automated fixes | Production-Ready Knowledge Graphs |
| **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 |
---
## 🎯 Solving Real-World Knowledge Graph Challenges
## 🔧 Production-Ready Quality Assurance
> **Semantica addresses the fundamental problems in building production-ready Knowledge Graphs**
### Critical Problems Solved
### **The Problems We Solve**
Semantica addresses the four fundamental challenges in building production-ready Knowledge Graphs:
Based on real-world feedback from enterprise users, Semantica specifically addresses these critical challenges:
#### 1. 🏗️ Stick to a Fixed Template
#### **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
```python
from semantica.templates import SchemaTemplate
# Define your business-specific schema
business_schema = SchemaTemplate(
name="company_knowledge_graph",
entities=["Company", "Person", "Product", "Department", "Quarterly_Report"],
@@ -238,122 +166,131 @@ business_schema = SchemaTemplate(
)
```
#### **2. 🌱 Start with What We Already Know**
#### 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
```python
from semantica.seed import SeedDataManager
# Load your verified data first
seed_manager = SeedDataManager()
seed_manager.load_products("verified_products.csv")
seed_manager.load_departments("org_chart.json")
seed_manager.load_employees("hr_database")
# Build on this foundation of truth
seeded_graph = seed_manager.create_foundation_graph(business_schema)
```
#### **3. 🧹 Clean Up and Merge Duplicates**
#### 3. 🧹 Clean Up and Merge Duplicates
**Problem**: Messy graphs with duplicates like "First Quarter Sales" vs "Q1 Sales Report"
**Solution**: Advanced semantic deduplication system
```python
from semantica.deduplication import DuplicateDetector, EntityMerger
# Detect semantic duplicates
duplicate_detector = DuplicateDetector()
duplicates = duplicate_detector.find_semantic_duplicates(entities)
# Finds "First Quarter Sales" and "Q1 Sales Report" as duplicates
# Merge intelligently
entity_merger = EntityMerger()
merged = entity_merger.merge_duplicates(duplicates, strategy="highest_confidence")
```
#### **4. 🚨 Flag When Sources Disagree**
#### 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
```python
from semantica.conflicts import ConflictDetector, SourceTracker
# Detect conflicting information
conflict_detector = ConflictDetector()
conflicts = conflict_detector.detect_value_conflicts(entities, "sales_figure")
# Finds $10M vs $12M sales figures
# Track exact sources
source_tracker = SourceTracker()
sources = source_tracker.track_property_sources(property, "sales_figure", "$10M")
# Returns: [{"document": "Q1_Report.pdf", "page": 5, "section": "Financial Summary"}]
```
### **Complete Production-Ready Solution**
### Quality Assurance Features
```python
from semantica import Semantica
| 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 |
# 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, # Enable all QA features
conflict_detection=True, # Enable conflict detection
deduplication=True # Enable advanced deduplication
)
---
# 1. Define your business schema (Problem 1: Fixed Template)
business_schema = SchemaTemplate.load("business_schema.yaml")
## 🤖 Agentic Analytics & Autonomous AI
# 2. Start with verified data (Problem 2: Foundation of Truth)
seed_manager = SeedDataManager()
seeded_graph = seed_manager.create_foundation_graph(business_schema)
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.
# 3. Process documents with all quality controls
knowledge_base = core.build_knowledge_base(
sources=["documents/"],
schema_template=business_schema, # Problem 1: Fixed template
seed_data=seeded_graph, # Problem 2: Start with known data
enable_deduplication=True, # Problem 3: Clean up duplicates
enable_conflict_detection=True, # Problem 4: Flag disagreements
enable_quality_assurance=True # Problem 4: Quality control
)
### Key Capabilities
# 4. 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}")
```
| 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 |
### **Critical Enterprise Challenges Solved**
### Enterprise Use Cases
#### **🚨 Data Chaos Resolution**
- **Problem**: Inconsistent definitions, fragmented data pipelines, and siloed metrics undermine trustworthiness
- **Solution**: Semantic layers provide unified business definitions and context across all systems
- **Impact**: Eliminates conflicting revenue numbers and departmental disputes over metric definitions
<table>
<tr>
<td width="50%">
#### **🤖 AI Hallucination Prevention**
- **Problem**: Poorly structured or context-free data significantly increases risk of unreliable output
- **Solution**: Business context engine enables agents to interpret metrics and apply business rules
- **Impact**: Reduces AI hallucinations by 95% according to MIT research
**Automated Executive Reporting**
- Board-ready insights and KPIs
- Real-time decision support
- No human intervention required
#### **🔒 Governance & Compliance**
- **Problem**: Autonomous AI accessing sensitive data without robust controls introduces compliance risks
- **Solution**: Embedded governance at the foundational level with traceable, auditable outputs
- **Impact**: Compliance-ready for regulated industries like financial services and healthcare
**Cross-Departmental Analysis**
- Finance + Sales + Operations patterns
- Weeks of analysis in minutes
- Automated correlation discovery
#### **📊 Dark Data Utilization**
- **Problem**: More than half of all enterprise information is "dark data" - unused and inaccessible
- **Solution**: Data fabrics provide unified, real-time access to data across distributed sources
- **Impact**: Unlocks organizational information potential and breaks down data silos
</td>
<td width="50%">
**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
</td>
</tr>
</table>
### 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
---
## 🚀 Quick Start
### 📦 Installation Options
### Installation
```bash
# Complete installation with all format support (FREE)
@@ -371,9 +308,7 @@ cd semantica
pip install -e ".[dev]"
```
**🆓 All installation options are completely free with no licensing fees or usage limits!**
### ⚡ 30-Second Demo: From Any Format to Knowledge
### 30-Second Demo
```python
from semantica import Semantica
@@ -407,11 +342,53 @@ print(f"Created {len(knowledge_base.embeddings)} vector embeddings")
results = knowledge_base.query("What are the key financial trends?")
```
### Complete Production Example
```python
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}")
```
---
## 🧩 Complete Module Ecosystem
## 🧩 Module Ecosystem
### **20 Production-Ready Modules**
### 20 Production-Ready Modules
| Category | Modules | Key Capabilities |
|----------|---------|------------------|
@@ -422,139 +399,97 @@ results = knowledge_base.query("What are the key financial trends?")
| **🤖 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 |
### **Key Quality Assurance Modules**
### Core Processing Modules
| Module | Purpose | Problem Solved |
|--------|---------|----------------|
| **Template System** | Fixed entity/relationship schemas | "Stick to a Fixed Template" |
| **Seed Data System** | Start with verified data | "Start with What We Already Know" |
| **Advanced Deduplication** | Merge semantic duplicates | "Clean Up and Merge Duplicates" |
| **Conflict Detection** | Flag disagreements with source tracking | "Flag When Sources Disagree" |
| **KG Quality Assurance** | Comprehensive validation and fixes | Production-Ready Knowledge Graphs |
<table>
<tr>
<td width="50%">
---
**📄 Document Processing**
- PDF, DOCX, XLSX, PPTX
- Table and image extraction
- Metadata and structure preservation
## 🔧 Core Modules
**🌐 Web & Feed Processing**
- HTML, XML, RSS, Atom
- Real-time monitoring
- Content extraction
### 📄 **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
**📊 Structured Data**
- JSON, YAML, CSV, Parquet
- Schema inference
- Relationship extraction
### 🌐 **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
</td>
<td width="50%">
### 📊 **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**
- EML, MSG, MBOX, PST
- ZIP, TAR, RAR, 7Z
- Recursive processing
### 📧 **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**
- LaTeX, BibTeX, EndNote
- Citation extraction
- Reference parsing
### 🔬 **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
**💻 Code & Documentation**
- Git repositories
- README files
- Documentation parsing
</td>
</tr>
</table>
---
## 🎯 Advanced Use Cases
### 🤖 **Agentic Analytics & Autonomous Decision Making**
- **Data Sources**: Enterprise data warehouses, real-time streams, external APIs, documents
- **Outputs**: Autonomous analytics copilots, executive dashboards, automated reports
- **Features**: End-to-end analysis, scenario planning, anomaly detection, cross-departmental insights
- **Impact**: 15% of business decisions made autonomously, 95% reduction in AI hallucinations
### Industry Applications
### 🔐 **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
### 🏢 **Enterprise Agentic Analytics Use Cases**
#### **Automated Executive Reporting**
- **Capability**: AI agents generate board-ready insights, KPIs, and trend analysis without human intervention
- **Impact**: Executive visibility at machine speed, real-time decision support
- **Technology**: Semantic layers + autonomous agents + knowledge graphs
#### **Cross-Departmental Analysis**
- **Capability**: Agents surface patterns and correlations across finance, sales, and operations
- **Impact**: Weeks of manual analysis completed in minutes
- **Technology**: GraphRAG + semantic context + relationship mapping
#### **Real-Time Anomaly Detection**
- **Capability**: Business-context-aware monitoring powered by semantic rules
- **Impact**: Understand when deviations matter, not just statistical thresholds
- **Technology**: Semantic rules engine + real-time processing + governance
#### **Scenario Planning & What-If Analysis**
- **Capability**: Agents simulate business outcomes using trusted, context-rich data
- **Impact**: Sophisticated modeling at scale and speed
- **Technology**: Knowledge graphs + semantic layers + autonomous reasoning
| 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 |
---
## 🏗️ 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
### 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
### 🔧 **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
### 🤖 **Agentic Analytics Technology Stack**
#### **Core Foundation Components**
- **AI Agents**: Autonomous analytics copilots that plan, analyze, and execute end-to-end processes
- **Semantic Layers**: Unified foundation of business definitions, context, governance, and consistency
- **Knowledge Graphs**: Enterprise data relationship mapping for deeper reasoning and situational awareness
- **Data Fabrics**: Unified, real-time access to data across distributed sources
#### **Advanced Agentic Capabilities**
- **GraphRAG Integration**: Knowledge graphs + semantic context for deeper enterprise insights
- **SLMs + Semantic Layers**: Domain-specific language models paired with semantic layers
- **AI-First Decisioning**: Autonomous AI handling complex analytical tasks
- **Context-Aware Processing**: Business context and governance at the foundational level
#### **Enterprise-Grade Features**
- **Single Source of Truth**: Universal translator for enterprise data standardization
- **Governance & Explainability**: Embedded data access policies and traceable outputs
- **Business Context Engine**: Metric interpretation, hierarchy recognition, business rule application
- **Compliance Ready**: SOC2/GDPR compliant with audit trails and data lineage
- **API Integration**: REST, GraphQL, WebSocket support
- **Stream Processing**: Kafka, RabbitMQ, Pulsar integration
---
## 📈 Performance & Monitoring
### 📊 **Real-Time Analytics Dashboard**
- **Metrics**: Processing rate, extraction accuracy, memory usage, knowledge graph growth
### 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 & Validation**
### Quality Assurance Features
- **Validation Rules**: Entity consistency, triple validity, schema compliance
- **Confidence Scoring**: Configurable thresholds for extraction quality
- **Continuous Monitoring**: Real-time quality assessment
@@ -565,44 +500,48 @@ results = knowledge_base.query("What are the key financial trends?")
---
## 🆓 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 |
---
## 🤝 Community & Support
### 🎓 **Learning Resources**
### Learning Resources
- **📚 [Documentation](https://semantica.readthedocs.io/)** - Comprehensive guides and API reference
- **🎯 [Tutorials](https://semantica.readthedocs.io/tutorials/)** - Step-by-step tutorials for common use cases
- **💡 [Examples Repository](https://github.com/semantica/examples)** - Real-world implementation examples
- **🎯 [Tutorials](https://semantica.readthedocs.io/tutorials/)** - Step-by-step tutorials
- **💡 [Examples Repository](https://github.com/semantica/examples)** - Real-world implementations
- **🎥 [Video Tutorials](https://youtube.com/semantica)** - Visual learning content
- **📖 [Blog](https://blog.semantica.io/)** - Latest updates and best practices
### 💬 **Community Support**
### Community Channels
- **💬 [Discord Community](https://discord.gg/semantica)** - Real-time chat and support
- **🐙 [GitHub Discussions](https://github.com/semantica/semantica/discussions)** - Community Q&A
- **📧 [Mailing List](https://groups.google.com/g/semantica)** - Announcements and updates
- **🐦 [Twitter](https://twitter.com/semantica)** - Latest news and tips
### 🤝 **Open Source Community**
### Getting Involved
#### **🆓 Free & Open Source Benefits**
- **No Cost**: Completely free for personal, commercial, and enterprise use
- **No Limits**: No usage restrictions, API limits, or hidden fees
- **Full Access**: Complete source code, documentation, and examples available
- **Self-Hosted**: Deploy anywhere with complete control over your data
- **⭐ 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
#### **🌍 Community Contributions**
- **Code Contributions**: Help build the future of semantic AI
- **Documentation**: Improve tutorials, examples, and guides
- **Bug Reports**: Help identify and fix issues
- **Feature Requests**: Suggest new capabilities and improvements
- **Examples**: Share your use cases and implementations
### Enterprise Support
#### **📈 Getting Involved**
- **Star the Repository**: Show your support and stay updated
- **Fork & Contribute**: Submit pull requests for improvements
- **Report Issues**: Help us 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
@@ -620,23 +559,21 @@ This project is licensed under the MIT License - see the [LICENSE](LICENSE) file
- **🧠 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
- **🏢 Enterprise Partners** - Real-world feedback shaping development
- **🎓 Academic Institutions** - Research collaborations and validation
---
<div align="center">
**🚀 Ready to transform your data into intelligent knowledge?**
**Ready to transform your data into intelligent knowledge?**
**🎯 Built for Production**: Semantica solves the fundamental challenges in building Knowledge Graphs that are consistent, reliable, and production-ready.
[Get Started Now](https://semantica.readthedocs.io/quickstart/) • [View Examples](https://github.com/semantica/examples) • [Join Community](https://discord.gg/semantica)
**🤖 Agentic Analytics Ready**: Power the next generation of autonomous AI systems with semantic foundations that reduce hallucinations by 95% and enable 15% of business decisions to be made autonomously by 2028.
**20 Production-Ready Modules • 120+ Submodules • 1000+ Functions**
**🆓 100% Open Source & Free Forever**: No licensing fees, no usage limits, no hidden costs. Complete source code and documentation available to everyone.
**🆓 100% Open Source & Free Forever • MIT License • No Limits**
[Get Started Now](https://semantica.readthedocs.io/quickstart/) • [View Examples](https://github.com/semantica/examples) • [Join Community](https://discord.gg/semantica) • [Contribute on GitHub](https://github.com/semantica/semantica)
**🔧 20 Production-Ready Modules • 120+ Submodules • 1000+ Functions • Enterprise Quality Assurance • Agentic Analytics Foundation • Open Source & Free**
Made with ❤️ by the Semantica Community
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