Semantica Framework - Technical Specification

1. Architecture Overview

1.1 System Architecture

Semantica follows a modular, layered architecture designed for extensibility and production deployment:

┌─────────────────────────────────────────────────────────────┐
│                    APPLICATION LAYER                         │
│  GraphRAG • AI Agents • Multi-Agent Systems • Analytics      │
└─────────────────────────────────────────────────────────────┘
                            ↓
┌─────────────────────────────────────────────────────────────┐
│              SEMANTIC PROCESSING LAYER                      │
│  Entity Extraction • Relation Extraction • Ontology Gen     │
│  Knowledge Graph Construction • Reasoning • QA               │
└─────────────────────────────────────────────────────────────┘
                            ↓
┌─────────────────────────────────────────────────────────────┐
│                  DATA PROCESSING LAYER                      │
│  Parsing • Normalization • Chunking • Embeddings            │
└─────────────────────────────────────────────────────────────┘
                            ↓
┌─────────────────────────────────────────────────────────────┐
│                   DATA INGESTION LAYER                       │
│  Files • Web • Feeds • Databases • Streams • Archives      │
└─────────────────────────────────────────────────────────────┘

1.2 Core Components

Orchestrator: Coordinates pipeline execution across modules
Config Manager: Centralized configuration with environment variable support
Plugin Registry: Extensible plugin system for custom components
Lifecycle Manager: Handles initialization, execution, and cleanup

2. Module Breakdown (29 Production Modules)

2.1 Core & Infrastructure (5 modules)

semantica.core - Framework orchestration, configuration management, plugin system

semantica.pipeline - Pipeline construction, execution, validation, parallelism management

semantica.utils - Shared utilities, validators, helpers, logging, exceptions

2.2 Data Processing (5 modules)

semantica.ingest - Universal data ingestion (50+ formats): files, web, feeds, databases, streams, emails, repositories

semantica.parse - Document parsing: PDF, DOCX, HTML, JSON, CSV, Excel, images, code, media

semantica.normalize - Data normalization: text cleaning, entity normalization, date/number formatting, encoding handling

semantica.split - Document chunking: semantic, structural, sliding window, table-aware chunking

2.3 Semantic Intelligence (4 modules)

semantica.semantic_extract - Entity recognition, relation extraction, event detection, triple extraction, coreference resolution

semantica.embeddings - Multi-modal embeddings: text, image, audio, with optimization and context management

semantica.ontology - 6-stage automatic ontology generation pipeline with symbolic validation (F1 up to 0.99)

2.4 Knowledge Graph (3 modules)

semantica.kg - Knowledge graph construction with temporal support, entity resolution, graph analytics, temporal queries

semantica.triple_store - RDF storage with adapters for Jena, Blazegraph, Virtuoso, RDF4J

semantica.vector_store - Vector storage with adapters for FAISS, Pinecone, Weaviate, Qdrant, Milvus

2.5 AI Applications (6 modules)

semantica.qa_rag - GraphRAG engine with hybrid retrieval (vector + graph), 30% accuracy improvement

semantica.context - Context engineering for AI agents: context graphs, agent memory, entity linking

semantica.reasoning - Reasoning & inference: deductive, abductive, SPARQL reasoning, RETE engine

2.6 Quality Assurance (5 modules)

semantica.deduplication - Entity deduplication with fuzzy matching, similarity calculation, merge strategies

semantica.conflicts - Conflict detection and resolution with source tracking and investigation guides

semantica.kg_qa - Knowledge graph quality assessment with comprehensive metrics

semantica.seed - Seed data management for building foundation graphs

3. Technology Stack

Category	Technologies
Core Language	Python 3.8+
NLP & ML	spaCy, Transformers, PyTorch, sentence-transformers, scikit-learn
Graph Processing	NetworkX, RDFLib, Neo4j, SPARQL
Vector Stores	FAISS, Pinecone, Weaviate, Qdrant, Milvus
Data Processing	Pandas, NumPy, BeautifulSoup4, lxml
Document Parsing	PyPDF2, python-docx, openpyxl, Pillow
Streaming	Kafka, RabbitMQ, Pulsar, Celery
Cloud & Storage	AWS (boto3), Azure, GCP, Redis, MongoDB, PostgreSQL

4. Data Flow Architecture

Input Sources
    ↓
[Ingestion Layer] → Parse → Normalize → Chunk
    ↓
[Semantic Extraction] → Entities → Relations → Events → Triples
    ↓
[Embedding Generation] → Vector Store
    ↓
[Knowledge Graph Construction] → Entity Resolution → Conflict Detection → Graph Store
    ↓
[Ontology Generation] → 6-Stage Pipeline → OWL/Turtle Export
    ↓
[Quality Assurance] → Deduplication → Conflict Resolution → Validation
    ↓
[Application Layer] → GraphRAG → AI Agents → Reasoning → Export

5. API Design Principles

5.1 High-Level API

from semantica import Semantica

core = Semantica(
    ner_model="transformer",
    relation_strategy="hybrid",
    graph_db="neo4j"
)

kg = core.build_knowledge_graph(sources=["documents/"])

5.2 Low-Level API

from semantica.semantic_extract import NamedEntityRecognizer
from semantica.kg import GraphBuilder

ner = NamedEntityRecognizer(model="transformer")
entities = ner.extract(text)

builder = GraphBuilder(merge_entities=True)
kg = builder.build(sources)

5.3 Plugin Architecture

Semantica supports custom plugins through the registry system:

from semantica.core import PluginRegistry

registry = PluginRegistry()
registry.register("custom_extractor", CustomExtractor)

6. Integration Points

Tool/Framework	Integration Type	Use Case
LangChain	Tool/Agent Integration	Semantic layer for AI agents
Haystack	RAG Pipeline	GraphRAG enhancement for retrieval
Neo4j	Graph Storage	Production knowledge graph storage
Stardog	RDF/OWL Storage	Ontology and triple store
Pinecone/Weaviate	Vector Storage	Embedding storage for semantic search
Apache Kafka	Stream Processing	Real-time knowledge graph updates

7. Performance Requirements

7.1 Processing Speed

Document Type	Current (docs/hour)	Target (docs/hour)
PDF (10 pages)	1,200	5,000
DOCX (5 pages)	2,500	10,000
HTML (articles)	5,000	20,000

7.2 Accuracy Metrics

Task	Current F1	Target F1
Entity Extraction	0.92	0.95
Relationship Extraction	0.87	0.90
Ontology Generation	0.94	0.97

7.3 Scalability Targets

Process 1M+ documents in under 24 hours
Support knowledge graphs with 100M+ entities
Real-time updates with <100ms latency
Concurrent query support for 1000+ users

8. Deployment Architecture

8.1 Single Node Deployment

┌─────────────────────────────────┐
│   Semantica Application        │
│   ├─ Orchestrator              │
│   ├─ Processing Modules        │
│   └─ Local Storage (FAISS)     │
└─────────────────────────────────┘

8.2 Distributed Deployment

┌──────────────┐  ┌──────────────┐  ┌──────────────┐
│   Worker 1   │  │   Worker 2  │  │   Worker N  │
│  (Ingestion) │  │ (Processing)│  │  (Graph)    │
└──────┬───────┘  └──────┬───────┘  └──────┬───────┘
       │                 │                 │
       └─────────────────┼─────────────────┘
                         │
              ┌──────────▼──────────┐
              │   Message Queue     │
              │   (Kafka/RabbitMQ)  │
              └──────────┬──────────┘
                         │
              ┌──────────▼──────────┐
              │   Graph Database    │
              │   (Neo4j/Neptune)   │
              └─────────────────────┘

9. Security & Privacy

Data Isolation: Namespace-based multi-tenancy
Access Control: Role-based permissions
Privacy: Support for federated learning and differential privacy
Encryption: Data at rest and in transit encryption

10. Open Standards Compliance

RDF: Full RDF 1.1 support with Turtle, N-Triples, JSON-LD
OWL: OWL 2 DL support with automatic ontology generation
SPARQL: SPARQL 1.1 query support
JSON-LD: JSON-LD 1.1 serialization
Schema.org: Schema.org vocabulary support

Document Version: 1.0 | Last Updated: 2025 | Semantica Framework