semantica/LatestReadme.md

🧠 Semantica

The AI-Native Semantic Layer & Knowledge Engineering Toolkit Modular • Agentic • RAG-Ready • Real-Time • Extensible • Open-Source

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🌐 Vision

In an AI-native world dominated by intelligent agents, automation pipelines, and reasoning systems, raw documents are no longer enough. To enable deep understanding, reasoning, memory, and automation — you need a semantic core.

Semantica is your central, extensible, open-source framework for transforming messy, unstructured data into machine-understandable, queryable, and actionable knowledge.

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🔧 What It Does

• ✅ Converts raw documents and streams to triplets, graphs, and embeddings
• ✅ Supports real-time ingestion from 100+ data sources and formats
• ✅ Enables knowledge graph generation, enrichment, and visualization
• ✅ Powers RAG, LLMs, multi-agent systems, and AI workflows
• ✅ Provides memory, reasoning, and semantic search capabilities
• ✅ Allows custom pipelines with DAG-style orchestration
• ✅ Cross-document linking and entity resolution
• ✅ Ontology alignment and schema mapping
• ✅ Context preservation across transformations

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📦 Installation

git clone https://github.com/semanticas/semcore.git
cd semcore
pip install -e .[all]  # Includes extras like UI, Whisper, Neo4j, etc.

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🏗️ Modular Architecture

📊 Data & Document Ingestion

Module	Description
semcore-crawler	Ingest HTML, PDFs, RSS feeds, YouTube, and web APIs
semcore-ocr	Extract text from images and scanned PDFs
semcore-docproc	Normalize, tokenize, clean, and structure documents
semcore-metadata	Extract metadata using rule-based, ML, and LLM approaches
semcore-asyncfetch	Parallel and cached fetching of large datasets
semcore-multiformat	Support for JSON, XML, YAML, CSV, Parquet, Avro, Excel
semcore-streaming	Real-time processing of Kafka, Pulsar, RabbitMQ streams
semcore-multimedia	Audio, video, image content extraction and processing
semcore-structured	Database, API, and tabular data ingestion
semcore-archives	ZIP, TAR, 7Z extraction and batch processing

🔍 Advanced Data Processing & Format Support

Module	Description
semcore-parser	Universal parser for 50+ file formats (DOCX, PPT, ODT, etc.)
semcore-converter	Cross-format conversion and normalization pipeline
semcore-validator	Schema validation and data quality assessment
semcore-transformer	Data transformation and ETL operations
semcore-splitter	Smart document splitting and boundary detection
semcore-merger	Document merging and concatenation with metadata preservation
semcore-annotator	Automatic annotation and markup generation
semcore-profiler	Data profiling and statistical analysis

🧬 Entity & Relationship Extraction

Module	Description
semcore-ner	Multi-model named entity recognition (spaCy, Stanza, BERT)
semcore-coref	Coreference resolution and entity linking
semcore-rel-extract	Relationship extraction using REBEL, OpenNRE, and LLMs
semcore-event-extract	Event detection and temporal relationship extraction
semcore-aspect-extract	Aspect-based sentiment and opinion mining
semcore-pattern-match	Rule-based pattern matching and regex extraction
semcore-joint-extract	Joint entity-relationship extraction models
semcore-weak-super	Weak supervision for entity labeling
semcore-active-learn	Active learning for improving extraction models
semcore-ensemble	Model ensemble and voting strategies

🗺️ Ontology & Schema Management

Module	Description
semcore-ontology-gen	Automatic ontology generation from text and data
semcore-onto-align	Ontology alignment and mapping between schemas
semcore-onto-merge	Ontology merging and conflict resolution
semcore-schema-infer	Automatic schema inference from unstructured data
semcore-owl-proc	OWL, RDFS, and SHACL processing and validation
semcore-taxonomy	Hierarchical taxonomy generation and management
semcore-concept-map	Concept mapping and semantic relationships
semcore-vocab-manage	Vocabulary management and standardization
semcore-mapping-learn	Machine learning-based schema mapping

🔗 Cross-Document Linking & Resolution

Module	Description
semcore-entity-link	Cross-document entity linking and disambiguation
semcore-deduplication	Semantic deduplication and entity resolution
semcore-cross-ref	Cross-reference detection and linking
semcore-canonical	Entity canonicalization and normalization
semcore-similarity	Document and entity similarity computation
semcore-clustering	Semantic clustering and grouping
semcore-record-link	Record linkage across heterogeneous sources
semcore-fuzzy-match	Fuzzy matching and approximate string matching
semcore-blocking	Efficient blocking strategies for large-scale linking

📝 Context Preservation & Semantic Processing

Module	Description
semcore-context-track	Context tracking across document transformations
semcore-semantic-chunk	Semantic-aware chunking and segmentation
semcore-discourse	Discourse analysis and rhetorical structure
semcore-narrative	Narrative structure extraction and timeline building
semcore-coherence	Coherence analysis and consistency checking
semcore-provenance	Data lineage and provenance tracking
semcore-citation	Citation extraction and reference linking
semcore-sentiment	Multi-level sentiment and emotion analysis
semcore-intent	Intent classification and purpose detection

📈 Graph Analytics & Knowledge Reasoning

Module	Description
semcore-graph-metrics	Graph centrality, clustering, and topological analysis
semcore-community	Community detection and graph partitioning
semcore-path-find	Shortest path and graph traversal algorithms
semcore-graph-embed	Graph embedding generation (Node2Vec, GraphSAGE, etc.)
semcore-reasoning	Logical reasoning and inference over knowledge graphs
semcore-subgraph	Subgraph extraction and pattern matching
semcore-anomaly	Graph anomaly detection and outlier identification
semcore-temporal-graph	Temporal graph analysis and evolution tracking
semcore-multi-layer	Multi-layer and multiplex graph analysis

🧠 Advanced Metadata Extraction

Module	Description
semcore-meta-rule	Rule-based metadata extraction with configurable patterns
semcore-meta-ml	Machine learning-based metadata classification
semcore-meta-llm	LLM-powered metadata generation and enrichment
semcore-meta-visual	Visual metadata extraction from images and layouts
semcore-meta-audio	Audio metadata and transcription analysis
semcore-meta-biblio	Bibliographic metadata extraction and normalization
semcore-meta-tech	Technical metadata (file properties, encoding, etc.)
semcore-meta-semantic	Semantic metadata (topics, themes, concepts)
semcore-meta-quality	Data quality and completeness assessment
semcore-meta-enrich	External metadata enrichment via APIs

🔢 Vector Stores & Embedding Management

Module	Description
semcore-vector-store	Multi-backend vector storage (Pinecone, Weaviate, Chroma)
semcore-embed-gen	Multi-model embedding generation and comparison
semcore-embed-meta	Metadata-aware embedding strategies
semcore-hybrid-search	Hybrid vector-keyword search with ranking fusion
semcore-embed-tune	Embedding fine-tuning and domain adaptation
semcore-vector-ops	Vector operations, clustering, and dimensionality reduction
semcore-index-manage	Vector index optimization and management
semcore-multi-modal	Multi-modal embedding for text, images, and structured data
semcore-embed-eval	Embedding quality evaluation and benchmarking

🧩 Knowledge Graph Construction

Module	Description
semcore-triplet-gen	Multi-approach triplet generation (rule, ML, LLM-based)
semcore-graph-build	Knowledge graph construction and validation
semcore-rdf-proc	RDF processing, serialization, and format conversion
semcore-neo4j-adapt	Neo4j adapter with Cypher query generation
semcore-sparql	SPARQL endpoint creation and query optimization
semcore-graph-merge	Multi-source graph merging and conflict resolution
semcore-quality	Knowledge graph quality assessment and validation
semcore-versioning	Graph versioning and change tracking
semcore-federation	Federated querying across distributed knowledge graphs

🤖 Advanced Agents & Reasoning

Module	Description
semcore-agent-kg	KG-aware agents for semantic tasks
semcore-reasoning	Logical and probabilistic reasoning engines
semcore-explanation	Explainable AI and reasoning path generation
semcore-planning	Semantic planning and goal-oriented reasoning
semcore-dialogue	Conversational agents with knowledge grounding
semcore-multiagent	Multi-agent coordination with shared semantic memory
semcore-tool-use	Tool-using agents with semantic understanding
semcore-fact-check	Automated fact-checking and verification

🔄 Pipeline & Orchestration

Module	Description
semcore-pipeline	DAG-based pipeline orchestration and workflow management
semcore-scheduler	Task scheduling and batch processing
semcore-monitor	Pipeline monitoring and performance analytics
semcore-cache	Intelligent caching and memoization strategies
semcore-parallel	Parallel processing and distributed computing
semcore-recovery	Error handling and recovery mechanisms
semcore-config	Configuration management and environment handling
semcore-testing	Unit testing and integration testing framework

🎯 Quality & Evaluation

Module	Description
semcore-benchmark	Comprehensive benchmarking and evaluation suite
semcore-metrics	Custom metrics for semantic tasks
semcore-validation	Data and model validation frameworks
semcore-ablation	Ablation studies and component analysis
semcore-compare	Model and pipeline comparison tools
semcore-regression	Regression testing for semantic pipelines
semcore-profiling	Performance profiling and optimization

🌐 Integration & Deployment

Module	Description
semcore-api	RESTful API and GraphQL endpoints
semcore-streaming-api	Real-time streaming API for live processing
semcore-docker	Containerization and deployment templates
semcore-k8s	Kubernetes operators and helm charts
semcore-cloud	Cloud provider integrations (AWS, GCP, Azure)
semcore-edge	Edge deployment and lightweight processing
semcore-federation	Federated deployment and cross-instance communication

💻 Developer Tools & UI

Module	Description
semcore-studio	Comprehensive web-based development environment
semcore-builder	Visual pipeline builder and editor
semcore-explorer	Interactive knowledge graph explorer
semcore-debugger	Semantic pipeline debugging and introspection
semcore-notebook	Jupyter notebook extensions and templates
semcore-cli	Command-line interface for all operations
semcore-sdk	Language SDKs (Python, JavaScript, Go, Rust)

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🚀 Core Features

🔄 Multi-Modal Processing

• Text, images, audio, video, and structured data
• Cross-modal semantic alignment and understanding
• Unified representation across modalities

🌍 Multi-Language Support

• 100+ language support for entity extraction
• Cross-lingual knowledge graph construction
• Language-agnostic semantic representations

⚡ Real-Time Processing

• Stream processing for live data ingestion
• Incremental knowledge graph updates
• Event-driven architecture with pub/sub

🔒 Privacy & Security

• PII detection and anonymization
• Differential privacy for sensitive data
• Secure multi-party computation for federated learning

📊 Scalability

• Horizontal scaling with distributed processing
• Memory-efficient processing for large datasets
• Cloud-native architecture with auto-scaling

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🧪 Example: Comprehensive Pipeline

from semcore.pipeline import SemanticPipeline
from semcore.ingestion import MultiFormatIngester
from semcore.extraction import EntityExtractor, RelationExtractor
from semcore.linking import CrossDocumentLinker
from semcore.ontology import OntologyAligner
from semcore.graph import KnowledgeGraphBuilder
from semcore.vectors import HybridVectorStore
from semcore.reasoning import SemanticReasoner

# Create comprehensive semantic processing pipeline
pipeline = SemanticPipeline()

# Multi-format data ingestion
ingester = MultiFormatIngester(
    sources=['web', 'pdf', 'json', 'xml', 'csv'],
    streaming=True,
    batch_size=1000
)

# Advanced entity and relationship extraction
extractor = EntityExtractor(
    models=['bert-ner', 'spacy', 'custom-llm'],
    ensemble_voting=True
)
rel_extractor = RelationExtractor(
    approaches=['rule-based', 'rebel', 'llm-guided']
)

# Cross-document linking and ontology alignment
linker = CrossDocumentLinker(
    similarity_threshold=0.85,
    blocking_strategy='semantic'
)
ontology = OntologyAligner(
    reference_ontologies=['dbpedia', 'wikidata', 'custom']
)

# Knowledge graph construction with quality validation
kg_builder = KnowledgeGraphBuilder(
    backend='neo4j',
    validation=True,
    versioning=True
)

# Hybrid vector store with metadata
vector_store = HybridVectorStore(
    embedding_models=['sentence-transformers', 'cohere'],
    metadata_strategy='hierarchical',
    hybrid_search=True
)

# Semantic reasoning engine
reasoner = SemanticReasoner(
    rules=['owl-reasoning', 'custom-logic'],
    explanation=True
)

# Assemble pipeline
pipeline.add_stages([
    ingester,
    extractor,
    rel_extractor,
    linker,
    ontology,
    kg_builder,
    vector_store,
    reasoner
])

# Execute with monitoring
results = pipeline.run(
    input_sources=['data/documents/', 'https://api.example.com'],
    monitoring=True,
    parallel=True
)

# Query the semantic layer
semantic_results = pipeline.query(
    "Find entities related to machine learning with high confidence scores",
    reasoning=True,
    explanation=True
)

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🔮 Advanced Capabilities

🧠 Contextual Understanding

• Semantic Chunking: Preserve meaning across document boundaries
• Discourse Analysis: Understand document structure and flow
• Context Propagation: Maintain context through processing pipeline
• Narrative Extraction: Build storylines and temporal sequences

🔗 Knowledge Integration

• Multi-Source Fusion: Combine knowledge from diverse sources
• Conflict Resolution: Handle contradictory information intelligently
• Incremental Updates: Efficiently update existing knowledge
• Provenance Tracking: Maintain full data lineage

📊 Advanced Analytics

• Graph Neural Networks: Deep learning on knowledge graphs
• Temporal Analysis: Track knowledge evolution over time
• Anomaly Detection: Identify unusual patterns and outliers
• Predictive Modeling: Forecast based on semantic patterns

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📊 Supported Data Formats & Sources

📄 Document Formats

• Text: TXT, MD, RTF, LaTeX
• Office: DOCX, PPTX, XLSX, ODT, ODS
• Web: HTML, XML, XHTML, RSS, Atom
• Archives: PDF, EPUB, MOBI
• Structured: JSON, YAML, TOML, INI
• Tabular: CSV, TSV, Parquet, Avro
• Multimedia: Images, Audio, Video files

🌐 Data Sources

• Web APIs (REST, GraphQL, SOAP)
• Databases (SQL, NoSQL, Graph)
• Message Queues (Kafka, RabbitMQ, Pulsar)
• Cloud Storage (S3, GCS, Azure Blob)
• Version Control (Git repositories)
• Enterprise Systems (SharePoint, Confluence)
• Social Media APIs
• Research Databases

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🛠️ Installation & Deployment

Quick Start

# Basic installation
pip install semantica

# Full installation with all features
pip install semantica[all]

# Specific modules
pip install semantica[graphs,vectors,ui]

Docker Deployment

# Start complete stack
docker-compose up -d semantica-stack

# Custom configuration
docker run -v ./config:/app/config semantica:latest

Kubernetes

# Deploy with Helm
helm install semantica ./charts/semantica

# Scale processing nodes
kubectl scale deployment semantica-workers --replicas=10

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🧑‍💻 Contributing

We welcome contributors to:

• 🔌 Add new data format parsers and connectors
• 🧠 Build advanced extraction models and algorithms
• 🗺️ Design ontology alignment strategies
• 🔗 Improve cross-document linking accuracy
• 📊 Develop graph analytics algorithms
• 🎨 Extend UI with annotation workflows
• 🏗️ Add database and vector store integrations
• 🧪 Create evaluation benchmarks and datasets
• 📚 Write documentation and tutorials

Check CONTRIBUTING.md and open a PR or issue to get started!

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📚 Documentation & Learning

• 📖 User Guide: Comprehensive tutorials and examples
• 🔧 API Reference: Complete API documentation
• 🏗️ Architecture Guide: System design and best practices
• 🧪 Cookbook: Common patterns and recipes
• 📊 Benchmarks: Performance comparisons and metrics
• 🎯 Use Cases: Real-world applications and case studies

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⚖️ License

MIT — Free for commercial and academic use. Attribution appreciated. Collaborative forks encouraged.

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🌟 Community & Support

• 💬 Discord: Real-time community chat
• 📧 Mailing List: Development discussions
• 🐛 GitHub Issues: Bug reports and feature requests
• 📚 Wiki: Community knowledge base
• 🎓 Workshops: Regular training sessions
• 🏆 Competitions: Semantic processing challenges

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✨ Join the Mission

Let's build the semantic backbone of the AI-native world.

Semantica is not just a toolkit — it's a movement toward AI systems that reason, explain, and learn from structured, semantic knowledge.

Connect. Contribute. Collaborate.

Together, we're creating the foundation for truly intelligent AI systems that understand not just what data says, but what it means.

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Inspired by: Diffbot, OpenIE, Haystack, REBEL, LangChain, RDFLib, Wikidata Toolkit, DeepLake, Trieve, spaCy, Hugging Face, Neo4j, Apache Jena