---
## π What is Semantica?
Semantica bridges the gap between raw data chaos and AI-ready knowledge. It's a **semantic intelligence platform** that transforms unstructured data into structured, queryable knowledge graphs powering GraphRAG, AI agents, and multi-agent systems.
### What Makes Semantica Different?
Unlike traditional approaches that process isolated documents and extract text into vectors, Semantica understands **semantic relationships across all content**, provides **automated ontology generation**, and builds a **unified semantic layer** with **production-grade QA**.
| **Traditional Approaches** | **Semantica's Approach** |
|:---------------------------|:-------------------------|
| Process data as isolated documents | **Understands semantic relationships across all content** |
| Extract text and store vectors | **Builds knowledge graphs with meaningful connections** |
| Generic entity recognition | **General-purpose ontology generation and validation** |
| Manual schema definition | **Automatic semantic modeling from content patterns** |
| Disconnected data silos | **Unified semantic layer across all data sources** |
| Basic quality checks | **Production-grade QA with conflict detection & resolution** |
---
## π― The Problem We Solve
### The Semantic Gap
Organizations today face a **fundamental mismatch** between how data exists and how AI systems need it.
#### The Semantic Gap: Problem vs. Solution
Organizations have **unstructured data** (PDFs, emails, logs), **messy data** (inconsistent formats, duplicates, conflicts), and **disconnected silos** (no shared context, missing relationships). AI systems need **clear rules** (formal ontologies), **structured entities** (validated, consistent), and **relationships** (semantic connections, context-aware reasoning).
| **What Organizations Have** | **What AI Systems Require** |
|:------------------------------|:------------------------------|
| **Unstructured Data** | **Clear Rules** |
| PDFs, emails, logs | Formal ontologies |
| Mixed schemas | Graphs & Networks |
| Conflicting facts | |
| **Messy, Noisy Data** | **Structured Entities** |
| Inconsistent formats | Validated entities |
| Duplicate records | Domain Knowledge |
| Missing relationships | |
| **Disconnected, Siloed Data** | **Relationships** |
| Data in separate systems | Semantic connections |
| No shared context | Context-Aware Reasoning |
| Isolated knowledge | |
### What Happens Without Semantics?
**They Break** β Systems crash due to inconsistent formats and missing structure.
**They Hallucinate** β AI models generate false information without semantic context to validate outputs.
**They Fail Silently** β Systems return wrong answers without warnings, leading to bad decisions.
**Why?** Systems have data β not semantics. They can't connect concepts, understand relationships, validate against domain rules, or detect conflicts.
### The Semantica Framework
Semantica operates through three integrated layers that transform raw data into AI-ready knowledge:
**Input Layer** β Universal ingestion from multiple data formats (PDFs, DOCX, HTML, JSON, CSV, databases, live feeds, APIs, streams, archives, multi-modal content) into a unified pipeline.
**Semantic Layer** β Core intelligence engine performing entity extraction, relationship mapping, ontology generation, context engineering, and quality assurance. Includes **advanced entity deduplication** (Jaro-Winkler, disjoint property handling) to ensure a clean single source of truth.
**Output Layer** β Production-ready knowledge graphs, vector embeddings, and validated ontologies that power GraphRAG systems, AI agents, and multi-agent systems.
**Powers: GraphRAG, AI Agents, Multi-Agent Systems**
#### Semantica Processing Flow
```mermaid
flowchart TD
A[Raw Data Sources
PDFs, Emails, Logs, Databases
Multiple Formats] --> B[Input Layer
Universal Data Ingestion]
B --> C[Format Detection
& Parsing]
C --> D[Normalization
& Preprocessing]
D --> E[Semantic Layer
Core Intelligence]
E --> F[Entity Extraction
NER + LLM Enhancement]
E --> G[Relationship Mapping
Triplet Generation]
E --> H[Ontology Generation
6-Stage Pipeline]
E --> I[Context Engineering
Semantic Enrichment]
E --> J[Quality Assurance
Conflict Detection]
F --> K[Output Layer]
G --> K
H --> K
I --> K
J --> K
K --> L[Knowledge Graphs
Production-Ready]
K --> M[Vector Embeddings
Semantic Search]
K --> N[Ontologies
OWL Validated]
L --> O[Application Layer]
M --> O
N --> O
O --> P[GraphRAG Engine
91% Accuracy]
O --> Q[AI Agents
Persistent Memory]
O --> R[Multi-Agent Systems
Shared Models]
O --> S[Analytics & BI
Graph Insights]
```
---
## π‘ The Semantica Solution
**Semantica** is an **open-source framework** that closes the semantic gap between real-world messy data and the structured semantic layers required by advanced AI systems β GraphRAG, agents, multi-agent systems, reasoning models, and more.
### How Semantica Solves These Problems
- :material-lightning-bolt: **Efficient Embeddings**
---
Uses **FastEmbed** by default for high-performance, lightweight local embedding generation (faster than sentence-transformers).
- :material-database-import: **Universal Data Ingestion**
---
Handles multiple formats (PDF, DOCX, HTML, JSON, CSV, databases, APIs, streams) with unified pipeline, no custom parsers needed.
- :material-brain: **Automated Semantic Extraction**
---
NER, relationship extraction, and triplet generation with LLM enhancement discovers entities and relationships automatically.
- :material-graph: **Knowledge Graph Construction**
---
Production-ready graphs with entity resolution, temporal support, and graph analytics. Queryable knowledge ready for AI applications.
- :material-robot: **GraphRAG Engine**
---
Hybrid vector + graph retrieval achieves **91% accuracy** (30% improvement) via semantic search + graph traversal for multi-hop reasoning.
- :material-account-cog: **AI Agent Context Engineering**
---
Persistent memory with RAG + knowledge graphs enables context maintenance, action validation, and structured knowledge access.
- :material-book-open-variant: **Automated Ontology Generation**
---
6-stage LLM pipeline generates validated OWL ontologies with HermiT/Pellet validation, eliminating manual engineering.
- :material-shield-check: **Production-Grade QA**
---
Conflict detection, deduplication, quality scoring, and provenance tracking ensure trusted, production-ready knowledge graphs.
- :material-cog-transfer: **Pipeline Orchestration**
---
Flexible pipeline builder with parallel execution enables scalable processing via orchestrator-worker pattern.
### Core Features at a Glance
| **Feature Category** | **Capabilities** | **Key Benefits** |
|:---------------------|:-----------------|:------------------|
| **Data Ingestion** | Multiple formats (PDF, DOCX, HTML, JSON, CSV, databases, APIs, streams, archives) | Universal ingestion, no custom parsers needed |
| **Semantic Extraction** | NER, relationship extraction, triplet generation, LLM enhancement | Automated discovery of entities and relationships |
| **Knowledge Graphs** | Entity resolution, temporal support, graph analytics, query interface | Production-ready, queryable knowledge structures |
| **Ontology Generation** | 6-stage LLM pipeline, OWL generation, HermiT/Pellet validation | Automated ontology creation from documents |
| **GraphRAG** | Hybrid vector + graph retrieval, multi-hop reasoning | 91% accuracy, 30% improvement over vector-only |
| **Agent Memory** | Persistent memory (Save/Load), Hybrid Retrieval (Vector+Graph), FastEmbed support | Context-aware agents with semantic understanding |
| **Pipeline Orchestration** | Parallel execution, custom steps, orchestrator-worker pattern | Scalable, flexible data processing |
| **Quality Assurance** | Conflict detection, deduplication, quality scoring, provenance | Trusted knowledge graphs ready for production |
---
## β¨ Core Capabilities
### 1. π Universal Data Ingestion
Process **multiple file formats** with intelligent semantic extraction:
- __π Documents__
---
- PDF (with OCR)
- DOCX, XLSX, PPTX
- TXT, RTF, ODT
- EPUB, LaTeX, Markdown
- __π Web & Feeds__
---
- HTML, XHTML, XML
- RSS, Atom feeds
- JSON-LD, RDFa
- Web scraping
- __πΎ Structured Data__
---
- JSON, YAML, TOML
- CSV, TSV, Excel
- Parquet, Avro, ORC
- SQL/NoSQL databases
- __π§ Communication__
---
- EML, MSG, MBOX
- PST archives
- Email threads
- Attachment extraction
- __ποΈ Archives__
---
- ZIP, TAR, RAR, 7Z
- Recursive processing
- Multi-level extraction
- __π¬ Scientific__
---
- BibTeX, EndNote, RIS
- JATS XML
- PubMed formats
- Citation networks
### 2. π§ Semantic Intelligence Engine
Transform raw text into structured semantic knowledge with state-of-the-art NLP and AI models:
- **Named Entity Recognition (NER)**: Extract people, organizations, locations, dates, and custom entities
- **Relationship Extraction**: Identify semantic, temporal, and causal relationships
- **Event Detection**: Detect and classify events (acquisitions, partnerships, announcements)
- **Coreference Resolution**: Resolve pronouns and entity mentions across documents
- **Triplet Extraction**: Generate RDF triplets for knowledge graph construction
### 3. πΈοΈ Knowledge Graph Construction
Build production-ready knowledge graphs with:
- **Automatic Entity Resolution**: Merge duplicate entities with fuzzy matching
- **Conflict Detection & Resolution**: Handle contradictory information from multiple sources
- **Temporal Knowledge Graphs**: Track changes over time with version history
- **Graph Analytics**: Centrality, community detection, path finding
- **Multi-Format Export**: Neo4j, RDF, JSON-LD, GraphML
### 4. π Ontology Generation & Management
Generate formal ontologies automatically using a **6-stage LLM-based pipeline**:
1. **Semantic Network Parsing** β Extract domain concepts
2. **YAML-to-Definition** β Transform into class definitions
3. **Definition-to-Types** β Map to OWL types
4. **Hierarchy Generation** β Build taxonomic structures
5. **TTL Generation** β Generate OWL/Turtle syntax
6. **Symbolic Validation** β HermiT/Pellet reasoning (F1 up to 0.99)
### 5. π Hybrid Search & Retrieval
Power GraphRAG applications with:
- **Vector Search**: Semantic similarity using embeddings
- **Graph Traversal**: Multi-hop reasoning for context expansion
- **Hybrid Retrieval**: Combine vector + graph for improved accuracy
- **Temporal Queries**: Query knowledge at specific time points
---
## π Choose Your Path
- :material-rocket-launch: **Quick Start**
---
Get up and running with Semantica in minutes. Learn the basics of ingestion and extraction.
[:arrow_right: Start Here](getting-started.md)
- :material-book-open-page-variant: **Core Concepts**
---
Deep dive into Knowledge Graphs, Ontologies, and Semantic Reasoning.
[:arrow_right: Learn Concepts](concepts.md)
- :material-code-braces: **API Reference**
---
Detailed technical documentation for all Semantica modules and classes.
[:arrow_right: View API](reference/core.md)
- :material-chef-hat: **Cookbook**
---
Interactive tutorials, real-world examples, and **14 domain-specific cookbooks**.
[:arrow_right: Explore Cookbook](cookbook.md)
---
## π¦ Installation
!!! success "Now Available on PyPI!"
Semantica is officially published on PyPI! Install it with a single command.
=== "From PyPI (Recommended)"
Install Semantica directly from PyPI:
```bash
# Install the core package
pip install semantica
# Or install with all optional dependencies
pip install semantica[all]
```
=== "From Source"
Install from the local source for the latest development version:
```bash
# Clone the repository
git clone https://github.com/Hawksight-AI/semantica.git
cd semantica
# Install in editable mode with core dependencies
pip install -e .
# Or install with all optional dependencies
pip install -e ".[all]"
```
=== "Development"
For contributors who want to modify the framework:
```bash
# Clone the repository
git clone https://github.com/Hawksight-AI/semantica.git
cd semantica
# Install in editable mode with dev dependencies
pip install -e ".[dev]"
```
=== "Docker"
Run Semantica in a containerized environment:
```bash
docker pull semantica/semantica:latest
docker run -it semantica/semantica
```
---
## π¦ Quick Example
Semantica uses a modular architecture. You can use individual modules directly for maximum flexibility:
```python
from semantica.ingest import FileIngestor
from semantica.parse import DocumentParser
from semantica.semantic_extract import NERExtractor, RelationExtractor
from semantica.kg import GraphBuilder
# 1. Ingest documents
ingestor = FileIngestor()
documents = ingestor.ingest_directory("documents/", recursive=True)
# 2. Parse documents
parser = DocumentParser()
parsed_docs = [parser.parse_document(doc) for doc in documents]
# 3. Extract entities and relationships
ner = NERExtractor()
rel_extractor = RelationExtractor()
entities = []
relationships = []
for doc in parsed_docs:
text = doc.get("full_text", "")
doc_entities = ner.extract_entities(text)
doc_rels = rel_extractor.extract_relations(text, entities=doc_entities)
entities.extend(doc_entities)
relationships.extend(doc_rels)
# 4. Build knowledge graph
builder = GraphBuilder(merge_entities=True)
kg = builder.build_graph(entities=entities, relationships=relationships)
print(f"Created graph with {len(kg.nodes)} nodes and {len(kg.edges)} edges")
```
!!! tip "Orchestration Option"
For complex workflows, you can also use the `Semantica` class for orchestration. See the [Core Module](reference/core.md) documentation for details.
---
## π― Why Semantica?
- **π 100% Open Source**
---
MIT licensed. No vendor lock-in. Full transparency.
- **π Production Ready**
---
Battle-tested with quality assurance, conflict resolution, and validation.
- **𧩠Modular Architecture**
---
Use only what you need. Swap components easily.
- **π Community Driven**
---
Built by developers, for developers. Active Discord community.
- **π Comprehensive**
---
End-to-end solution from ingestion to reasoning. No duct-taping required.
- **π¬ Research-Backed**
---
Based on latest research in knowledge graphs, ontologies, and semantic web.
---
## ποΈ Built For
- **Data Scientists**: Transform messy data into clean knowledge graphs
- **Data Engineers**: Build scalable data pipelines with semantic enrichment
- **AI Engineers**: Build GraphRAG, AI agents, and multi-agent systems
- **Knowledge Engineers**: Generate and manage formal ontologies
- **Ontologists**: Design and validate domain-specific ontologies and taxonomies
- **Researchers**: Analyze scientific literature and build citation networks
- **ML Engineers**: Create semantic features for machine learning models
- **Enterprises**: Unify data silos into a semantic layer
---
## π Learn More
- [Getting Started Guide](getting-started.md) - Your first knowledge graph in 5 minutes
- [Core Concepts](concepts.md) - Deep dive into knowledge graphs and ontologies
- [Cookbook](cookbook.md) - Real-world examples and **14 domain-specific cookbooks**
- [API Reference](reference/core.md) - Complete technical documentation
### π³ Recommended Cookbook Tutorials
Get hands-on with interactive Jupyter notebooks:
- **[Welcome to Semantica](https://github.com/Hawksight-AI/semantica/blob/main/cookbook/introduction/01_Welcome_to_Semantica.ipynb)**: Comprehensive introduction to all Semantica modules
- **Topics**: Framework overview, all modules, architecture
- **Difficulty**: Beginner
- **Use Cases**: First-time users, understanding the framework
- **[Your First Knowledge Graph](https://github.com/Hawksight-AI/semantica/blob/main/cookbook/introduction/08_Your_First_Knowledge_Graph.ipynb)**: Build your first knowledge graph from scratch
- **Topics**: Entity extraction, relationship extraction, graph construction
- **Difficulty**: Beginner
- **Use Cases**: Learning the basics, quick start
- **[GraphRAG Complete](https://github.com/Hawksight-AI/semantica/blob/main/cookbook/use_cases/advanced_rag/01_GraphRAG_Complete.ipynb)**: Production-ready Graph Retrieval Augmented Generation
- **Topics**: GraphRAG, hybrid retrieval, vector search, graph traversal
- **Difficulty**: Advanced
- **Use Cases**: Building AI applications with knowledge graphs
---
**Ready to transform your data into knowledge?**
[Get Started Now](getting-started.md){ .md-button .md-button--primary }
[Join Discord](https://discord.gg/semantica){ .md-button }
