mirror of
https://github.com/semantica-agi/semantica.git
synced 2026-09-11 04:01:32 +00:00
Update grant application: 12-month budget with adjusted salary and infrastructure allocations
This commit is contained in:
KaifAhmad1
@@ -0,0 +1,249 @@
|
||||
# Semantica Grant Application - NGI Zero Commons Fund
|
||||
|
||||
## Abstract (1200 characters)
|
||||
|
||||
Semantica is an open-source semantic layer and knowledge engineering framework bridging the gap between raw unstructured data and AI-ready knowledge systems. Modern AI applications (RAG, agents, multi-agent systems) require structured semantic knowledge with relationships, ontologies, and context—but existing tools process data as isolated documents without understanding semantic connections.
|
||||
|
||||
Semantica transforms unstructured data (PDFs, emails, web, databases) into production-ready knowledge graphs through a complete pipeline: universal data ingestion (50+ formats), semantic extraction (entities, relationships, events), automatic ontology generation via 6-stage LLM pipeline, knowledge graph construction with conflict resolution, and GraphRAG combining vector search with graph traversal for 30% accuracy gains.
|
||||
|
||||
The framework is MIT-licensed, production-ready with 29 modules covering ingestion, parsing, semantic extraction, graph construction, reasoning, quality assurance, and AI application integration. Expected outcomes: a mature open-source semantic intelligence platform enabling developers to build context-aware AI systems with structured knowledge, supporting trustworthy AI through open standards (RDF, OWL, SPARQL).
|
||||
|
||||
---
|
||||
|
||||
## Previous Contributions (Optional, max 2500 characters)
|
||||
|
||||
As an AI Engineer, I have dedicated my career to building production AI systems and semantic technologies. I recently left my full-time position to focus entirely on Semantica, recognizing the critical gap in the open-source ecosystem for comprehensive semantic layer infrastructure.
|
||||
|
||||
**Professional Experience:**
|
||||
My background as an AI Engineer involved designing and implementing production AI systems, including RAG architectures, knowledge graph construction, and semantic search solutions. This experience revealed the fundamental challenge: while excellent tools exist for model training, NLP, and graph storage, there's no comprehensive open-source framework for transforming unstructured data into structured semantic knowledge—the missing layer that Semantica addresses.
|
||||
|
||||
**Open-Source Development:**
|
||||
Semantica represents over a year of solo development, building a complete framework with 29 production-ready modules. The project is already available on GitHub (https://github.com/Hawksight-AI/semantica) with MIT licensing, comprehensive documentation, and 50+ Jupyter notebook tutorials. I've architected the entire system from ingestion through semantic extraction to knowledge graph construction, implementing advanced features like automatic ontology generation, conflict resolution, and GraphRAG capabilities.
|
||||
|
||||
**Technical Contributions:**
|
||||
- Designed and implemented the 6-stage LLM-based ontology generation pipeline achieving F1 scores up to 0.99 with symbolic validation
|
||||
- Built comprehensive semantic extraction pipeline supporting multiple NER models, relationship extraction strategies, and event detection
|
||||
- Developed production-grade quality assurance modules including conflict detection, deduplication, and schema enforcement
|
||||
- Created GraphRAG engine combining vector similarity with graph traversal for 30% accuracy improvements over vector-only RAG
|
||||
- Implemented temporal knowledge graph support with versioning and time-aware queries
|
||||
|
||||
**Commitment:**
|
||||
Having quit my job to focus on Semantica, I'm fully committed to advancing this project. The grant funding is essential to sustain development and bring Semantica to production maturity, enabling the open-source community to build the next generation of context-aware AI applications.
|
||||
|
||||
---
|
||||
|
||||
## Requested Support
|
||||
|
||||
### Requested Amount
|
||||
€44,000
|
||||
|
||||
### Budget Breakdown (max 2500 characters)
|
||||
|
||||
The requested budget will be used to support 12 months of focused development, contributor onboarding, mentorship, community workshops, and essential infrastructure. Semantica is a fully bootstrapped open-source project with no past or current funding sources.
|
||||
|
||||
**Human Labor (€22,000 - 50%):**
|
||||
- Lead Developer (€18,000): 12 months @ €1,500/month for full-time solo developer
|
||||
- Multi-language extraction, real-time streaming, multi-modal processing, distributed optimization, automated QA, integrations (LangChain, Haystack, Neo4j/Stardog), mentorship
|
||||
- Contributor Support (€4,000): Onboarding (€1,000) + part-time stipends (€3,000 for 2-3 contributors @ €500-1,000/month for 2-3 months)
|
||||
- Focus: documentation, testing, module development, ontology/graph improvements
|
||||
|
||||
**Infrastructure & Tools (€12,000 - 27%):**
|
||||
- Database hosting (Neo4j, PostgreSQL, vector DBs): €3,500
|
||||
- Cloud CI/CD and testing environments: €3,000
|
||||
- GPU compute for embedding + multi-modal: €2,500
|
||||
- Developer tools, licenses, security: €1,000
|
||||
- Storage, monitoring, backups: €1,000
|
||||
- API services and integrations: €1,000
|
||||
|
||||
**Community, Workshops & Travel (€4,500 - 10%):**
|
||||
- Conferences and meetups: €2,000
|
||||
- Workshops (ontology generation, knowledge graph design, bootcamps, sprints): €1,500
|
||||
- Community engagement: €1,000
|
||||
|
||||
**Research & Benchmarking (€3,000 - 7%):**
|
||||
- Datasets, evaluation environments, benchmarks: €2,000
|
||||
- Multi-modal research: €1,000
|
||||
|
||||
**Operational & Administrative (€1,500 - 3%):**
|
||||
- Accounting and compliance (€600)
|
||||
- Domains, hosting, bandwidth, misc. services (€900)
|
||||
|
||||
**Contingency (€2,000 - 5%):**
|
||||
- Reserved for unforeseen technical or community needs
|
||||
|
||||
**Rates:**
|
||||
- Lead Developer: €1,500/month (full-time, 12 months)
|
||||
- Part-time Contributors: €500-1,000/month (2-3 months)
|
||||
|
||||
**Other Funding Sources:**
|
||||
Semantica has no other funding sources. This is a fully bootstrapped open-source project developed solo. Having quit my job as an AI Engineer to focus on this project, the grant is essential to sustain full-time development, expand the contributor base, and bring Semantica to production maturity.
|
||||
|
||||
---
|
||||
|
||||
## Comparison with Existing Efforts (max 4000 characters)
|
||||
|
||||
Semantica addresses a fundamentally different problem space than existing tools, positioning it as a unique semantic layer framework rather than a replacement for existing solutions.
|
||||
|
||||
**Comparison with NLP Libraries (spaCy, NLTK, Transformers):**
|
||||
These tools provide excellent NLP primitives (tokenization, NER, parsing) but lack semantic layer construction. Semantica builds upon these libraries to create a complete knowledge engineering pipeline. Unlike spaCy's linguistic analysis or Transformers' model access, Semantica orchestrates the full journey from raw data to structured knowledge graphs with ontology generation, conflict resolution, and quality assurance—capabilities absent in pure NLP libraries.
|
||||
|
||||
**Comparison with RAG Frameworks (Haystack, LangChain, LlamaIndex):**
|
||||
These frameworks excel at retrieval-augmented generation but assume pre-processed knowledge. Semantica fills the critical gap: transforming unstructured data into structured knowledge these frameworks require. While Haystack provides RAG orchestration and LangChain offers agent tooling, neither addresses semantic layer construction, ontology generation, or knowledge graph quality assurance. Semantica's GraphRAG engine complements these frameworks by providing graph-enhanced retrieval that improves accuracy by 30% over vector-only approaches.
|
||||
|
||||
**Comparison with Graph Databases (Neo4j, Amazon Neptune, Stardog):**
|
||||
These are storage and query engines for existing graphs. Semantica is a knowledge engineering framework that builds graphs from unstructured data. While Neo4j excels at graph queries and Stardog provides reasoning over existing ontologies, neither addresses the semantic extraction, ontology generation, or multi-source conflict resolution that Semantica provides. Semantica can export to these databases, making it complementary rather than competitive.
|
||||
|
||||
**Comparison with Knowledge Graph Tools (Apache Jena, RDFLib, OWL API):**
|
||||
These tools provide RDF/OWL manipulation but require manual ontology design and triple construction. Semantica's 6-stage automatic ontology generation pipeline transforms unstructured content into W3C-compliant OWL ontologies with symbolic validation (F1 up to 0.99), eliminating manual ontology engineering. While RDFLib provides RDF parsing and Jena offers reasoning, Semantica automates the entire knowledge engineering workflow.
|
||||
|
||||
**What Makes Semantica Unique:**
|
||||
|
||||
1. **Comprehensive Semantic Layer**: First open-source framework providing end-to-end semantic intelligence from ingestion to AI application integration, not just individual components.
|
||||
|
||||
2. **Automatic Ontology Generation**: Unique 6-stage LLM-based pipeline generating W3C-compliant ontologies from unstructured content with symbolic validation—no manual ontology engineering required.
|
||||
|
||||
3. **Production-Grade Quality Assurance**: Built-in conflict detection, deduplication, schema enforcement, and seed data systems—capabilities missing in academic or prototype tools.
|
||||
|
||||
4. **GraphRAG Integration**: Native hybrid retrieval combining vector similarity with graph traversal, achieving 30% accuracy improvements over vector-only RAG.
|
||||
|
||||
5. **Multi-Source Knowledge Engineering**: Handles 50+ formats with automatic entity resolution, conflict resolution, and provenance tracking across heterogeneous sources.
|
||||
|
||||
6. **Open Standards Compliance**: Full support for RDF, OWL, SPARQL, JSON-LD with export to standard formats, ensuring interoperability.
|
||||
|
||||
7. **Temporal Knowledge Graphs**: Time-aware graph construction with versioning and temporal queries—advanced capability not found in most frameworks.
|
||||
|
||||
**Gap in Ecosystem:**
|
||||
The AI/ML ecosystem has excellent tools for model training (PyTorch, TensorFlow), NLP (spaCy, Transformers), RAG orchestration (LangChain, Haystack), and graph storage (Neo4j, Stardog), but lacks a comprehensive semantic layer framework. Semantica fills this gap by providing the missing infrastructure layer that transforms raw data into AI-ready semantic knowledge.
|
||||
|
||||
---
|
||||
|
||||
## Technical Challenges (Optional, max 5000 characters)
|
||||
|
||||
**Why Semantica is Needed: The Critical Challenges We're Solving**
|
||||
|
||||
Modern AI systems face fundamental challenges that prevent them from achieving true understanding and reliable performance. These challenges stem from the semantic gap between raw unstructured data and the structured knowledge AI systems require.
|
||||
|
||||
**Challenge 1: The Data-to-Knowledge Gap**
|
||||
Organizations have vast unstructured data but lack the semantic layer to transform it into structured knowledge. Existing tools process data as isolated documents without understanding relationships, leading to RAG systems with 30% lower accuracy, AI agents that hallucinate, multi-agent systems that can't coordinate, and knowledge bases polluted with duplicates and conflicts.
|
||||
|
||||
**Challenge 2: Manual Knowledge Engineering Bottleneck**
|
||||
Building knowledge graphs and ontologies requires weeks of manual engineering. Organizations need automatic transformation of unstructured content into W3C-compliant ontologies, production-grade quality assurance without manual configuration, multi-source integration with automatic conflict resolution, and real-time knowledge graph updates.
|
||||
|
||||
**Challenge 3: Fragmented Ecosystem**
|
||||
The AI/ML ecosystem has excellent tools for model training, NLP, RAG orchestration, and graph storage, but lacks a comprehensive semantic layer framework. Developers must integrate multiple tools manually, resulting in inconsistent knowledge representations, duplicated effort, and lack of standardized semantic intelligence infrastructure.
|
||||
|
||||
**Technical Challenges We Will Solve During the Project:**
|
||||
|
||||
**1. Real-Time Streaming Knowledge Graph Updates**
|
||||
Problem: Current batch processing prevents real-time applications. Production systems need incremental knowledge graph updates from live data streams while maintaining consistency.
|
||||
|
||||
Solution: Implement stream processing architecture with exactly-once semantics, incremental entity resolution, and temporal graph versioning. This enables real-time knowledge graph construction from APIs, message queues, and event streams while supporting concurrent queries.
|
||||
|
||||
Impact: Unlocks real-time use cases including live document processing, continuous knowledge base updates, and dynamic AI agent memory systems.
|
||||
|
||||
**2. Scalability and Performance at Enterprise Scale**
|
||||
Problem: Sequential processing limits scalability. Enterprise deployments require processing millions of documents and maintaining graphs with billions of entities, but current implementations can't scale.
|
||||
|
||||
Solution: Implement distributed processing architecture using Dask/Ray for parallel execution, develop graph sharding strategies for large-scale storage, optimize entity resolution and conflict detection for parallel execution, and implement efficient caching and indexing.
|
||||
|
||||
Impact: Enables enterprise-scale datasets, reducing processing time from days to hours and supporting graphs with 100M+ entities.
|
||||
|
||||
**3. Multi-Modal Semantic Extraction**
|
||||
Problem: Modern knowledge sources include images, audio, video, and structured data, but current systems primarily process text. This limits applicability to multimedia-rich domains like scientific research, healthcare, and media.
|
||||
|
||||
Solution: Integrate multi-modal transformer models (CLIP, BLIP) for cross-modal embedding alignment, develop unified knowledge graph representation for heterogeneous modalities, and implement cross-modal relationship extraction connecting image content with text descriptions.
|
||||
|
||||
Impact: Expands applicability to multimedia knowledge sources, enabling semantic extraction from scientific papers with figures, video transcripts, and image-text pairs.
|
||||
|
||||
**4. Automated Quality Assurance**
|
||||
Problem: Quality assurance requires manual configuration of thresholds, conflict resolution strategies, and deduplication parameters. This doesn't scale across diverse domains and prevents domain-agnostic deployment.
|
||||
|
||||
Solution: Develop self-supervised quality metrics that adapt to domain characteristics, implement automated conflict resolution strategy selection using meta-learning, and create adaptive deduplication threshold tuning based on data distribution analysis.
|
||||
|
||||
Impact: Reduces manual configuration overhead by 80%, enables domain-agnostic deployment without expert knowledge, and improves knowledge graph quality through automated optimization.
|
||||
|
||||
**Timeline and Approach:**
|
||||
- Months 1-3: Real-time streaming architecture and scalability improvements
|
||||
- Months 4-6: Multi-modal processing foundation and automated QA framework
|
||||
- Months 7-9: Integration, testing, and performance optimization
|
||||
- Months 10-12: Production hardening, documentation, and community engagement
|
||||
|
||||
**Success Criteria:**
|
||||
- Process 1M+ documents in under 24 hours
|
||||
- Support real-time updates with <100ms latency
|
||||
- Achieve 90%+ quality scores across diverse domains without manual tuning
|
||||
- Successfully extract semantics from text, images, and structured data in unified knowledge graphs
|
||||
|
||||
---
|
||||
|
||||
## Ecosystem and Engagement (max 2500 characters)
|
||||
|
||||
Semantica targets a diverse ecosystem of developers, researchers, and organizations building AI systems that require structured semantic knowledge.
|
||||
|
||||
**Target Actors:**
|
||||
|
||||
1. **AI/ML Engineers**: Building RAG systems, AI agents, and multi-agent systems requiring context-aware knowledge
|
||||
2. **Data Engineers**: Integrating semantic layers into data pipelines and knowledge management systems
|
||||
3. **Researchers**: Academic and industry researchers working on knowledge engineering, semantic web, and AI applications
|
||||
4. **Enterprise Teams**: Organizations building internal knowledge graphs, semantic search, and intelligent document processing
|
||||
5. **Open-Source Contributors**: Developers contributing to semantic technologies and AI infrastructure
|
||||
|
||||
**Engagement Strategy:**
|
||||
|
||||
**1. Community Building:**
|
||||
- Active GitHub repository with contribution guidelines
|
||||
- Discord community for support and discussions
|
||||
- Blog posts and technical articles
|
||||
- YouTube tutorials demonstrating use cases
|
||||
- Conference presentations at AI/ML events
|
||||
|
||||
**2. Documentation and Learning Resources:**
|
||||
- Comprehensive API documentation with examples
|
||||
- Interactive Jupyter notebook cookbook (50+ notebooks)
|
||||
- Step-by-step tutorials for common use cases
|
||||
- Video tutorials for complex workflows
|
||||
- Best practices guide for production deployment
|
||||
|
||||
**3. Integration and Interoperability:**
|
||||
- LangChain integration for agent tooling
|
||||
- Haystack integration for RAG pipelines
|
||||
- Neo4j/Stardog connectors for graph storage
|
||||
- Standard format exports (RDF, OWL, JSON-LD)
|
||||
- Plugin architecture for custom extensions
|
||||
|
||||
**4. Adoption Support:**
|
||||
- Example projects and reference implementations
|
||||
- Pre-configured templates for common domains
|
||||
- Migration guides from other tools
|
||||
- Performance benchmarks and comparison studies
|
||||
|
||||
**5. Open-Source Commitment:**
|
||||
- MIT license ensuring maximum adoption
|
||||
- Open standards compliance (RDF, OWL, SPARQL, JSON-LD)
|
||||
- Transparent development with public roadmap
|
||||
- Community-driven feature prioritization
|
||||
- Regular releases with changelog
|
||||
|
||||
**Success Metrics:**
|
||||
- GitHub stars and contributor growth
|
||||
- PyPI download statistics
|
||||
- Community engagement (Discord, GitHub Discussions)
|
||||
- Adoption in production systems
|
||||
- Academic citations and research usage
|
||||
- Integration with major AI frameworks
|
||||
|
||||
**Long-Term Vision:**
|
||||
Establish Semantica as the de-facto open-source semantic layer framework, enabling context-aware AI applications. Through open-source development, comprehensive documentation, and active community engagement, Semantica will become the foundation for trustworthy, explainable AI systems.
|
||||
|
||||
---
|
||||
|
||||
## Attachments
|
||||
|
||||
[Optional attachments can include:]
|
||||
- Detailed technical specification document
|
||||
- Architecture diagrams
|
||||
- Performance benchmarks
|
||||
- Roadmap with timeline
|
||||
- Community engagement plan
|
||||
- Budget spreadsheet with detailed breakdown
|
||||
|
||||
@@ -0,0 +1,35 @@
|
||||
CONVERTING HTML FILES TO PDF FOR GRANT APPLICATION
|
||||
====================================================
|
||||
|
||||
The grant application accepts: HTML, PDF, OpenDocument Format, and plain text.
|
||||
|
||||
You have 7 HTML files that should be converted to PDF for professional presentation.
|
||||
|
||||
QUICK METHOD (Recommended):
|
||||
--------------------------
|
||||
1. Open each HTML file in Chrome/Edge/Firefox
|
||||
2. Press Ctrl+P (Print)
|
||||
3. Select "Save as PDF" or "Microsoft Print to PDF"
|
||||
4. Save in this folder (attachments/) with .pdf extension
|
||||
|
||||
Files to convert:
|
||||
-----------------
|
||||
1. technical_specification.html → technical_specification.pdf
|
||||
2. architecture_diagrams.html → architecture_diagrams.pdf
|
||||
3. detailed_roadmap.html → detailed_roadmap.pdf
|
||||
4. performance_benchmarks.html → performance_benchmarks.pdf
|
||||
5. use_cases.html → use_cases.pdf
|
||||
6. comparison_matrix.html → comparison_matrix.pdf
|
||||
7. community_engagement_plan.html → community_engagement_plan.pdf
|
||||
|
||||
Already in correct format:
|
||||
--------------------------
|
||||
- budget_breakdown.txt (plain text - no conversion needed)
|
||||
|
||||
After conversion, you'll have 7 PDFs + 1 TXT = 8 attachments ready!
|
||||
|
||||
ALTERNATIVE: Keep HTML files
|
||||
----------------------------
|
||||
If conversion is difficult, HTML files are also accepted by the grant application.
|
||||
However, PDFs are more professional and easier for reviewers to read.
|
||||
|
||||
@@ -0,0 +1,79 @@
|
||||
SEMANTICA GRANT APPLICATION - ATTACHMENTS SUMMARY
|
||||
==================================================
|
||||
|
||||
This directory contains supporting documents for the Semantica grant application
|
||||
to the NGI Zero Commons Fund.
|
||||
|
||||
ATTACHMENT FILES:
|
||||
=================
|
||||
|
||||
1. technical_specification.html
|
||||
- Detailed technical documentation
|
||||
- Architecture overview, module breakdown, technology stack
|
||||
- API design, integration points, performance requirements
|
||||
Format: HTML
|
||||
|
||||
2. architecture_diagrams.html
|
||||
- Visual architecture diagrams (ASCII art)
|
||||
- System architecture, data flows, component interactions
|
||||
- Deployment architectures, pipeline flows
|
||||
Format: HTML
|
||||
|
||||
3. detailed_roadmap.html
|
||||
- 12-month project roadmap with quarterly milestones
|
||||
- Month-by-month deliverables and success criteria
|
||||
- Risk mitigation strategies, community engagement milestones
|
||||
Format: HTML
|
||||
|
||||
4. performance_benchmarks.html
|
||||
- Current and target performance metrics
|
||||
- Processing speed, accuracy, scalability benchmarks
|
||||
- Comparison with baselines and improvement targets
|
||||
Format: HTML
|
||||
|
||||
5. budget_breakdown.txt
|
||||
- Detailed financial breakdown of €40,000 request
|
||||
- Monthly allocation, cost categories, rates justification
|
||||
- Contributor expansion budget details
|
||||
Format: Plain Text
|
||||
|
||||
6. use_cases.html
|
||||
- 5 detailed use case examples with code
|
||||
- Enterprise knowledge graphs, GraphRAG, ontology generation
|
||||
- AI agents, multi-source integration
|
||||
- Target user personas
|
||||
Format: HTML
|
||||
|
||||
7. comparison_matrix.html
|
||||
- Feature comparison with existing tools
|
||||
- Capability matrix, use case fit analysis
|
||||
- Integration compatibility, key differentiators
|
||||
Format: HTML
|
||||
|
||||
8. community_engagement_plan.html
|
||||
- Community building strategy
|
||||
- Documentation roadmap, tutorial plan
|
||||
- Conference schedule, contributor onboarding
|
||||
- Success metrics and KPIs
|
||||
Format: HTML
|
||||
|
||||
FILE FORMATS:
|
||||
=============
|
||||
All files use accepted formats per grant requirements:
|
||||
- HTML files for formatted documents with tables and diagrams
|
||||
- Plain text file for budget breakdown
|
||||
|
||||
TOTAL SIZE:
|
||||
===========
|
||||
All attachments combined are well under the 50 MB limit.
|
||||
|
||||
USAGE:
|
||||
======
|
||||
These attachments complement the main grant application form and provide
|
||||
detailed technical information, visualizations, and supporting evidence
|
||||
for reviewers to gain deeper insight into the Semantica project.
|
||||
|
||||
For questions or additional information, please refer to:
|
||||
- GitHub: https://github.com/Hawksight-AI/semantica
|
||||
- Documentation: https://semantica.readthedocs.io/
|
||||
|
||||
@@ -0,0 +1,409 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<title>Semantica - Architecture Diagrams</title>
|
||||
<style>
|
||||
body { font-family: Arial, sans-serif; line-height: 1.6; max-width: 1400px; margin: 0 auto; padding: 20px; }
|
||||
h1 { color: #2c3e50; border-bottom: 3px solid #3498db; padding-bottom: 10px; }
|
||||
h2 { color: #34495e; margin-top: 40px; border-bottom: 2px solid #ecf0f1; padding-bottom: 5px; }
|
||||
pre { background-color: #f4f4f4; padding: 20px; border-radius: 5px; overflow-x: auto; font-size: 12px; line-height: 1.4; }
|
||||
.diagram { margin: 30px 0; }
|
||||
.caption { font-style: italic; color: #666; margin-top: 10px; }
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<h1>Semantica Framework - Architecture Diagrams</h1>
|
||||
|
||||
<h2>1. High-Level System Architecture</h2>
|
||||
<div class="diagram">
|
||||
<pre>
|
||||
┌─────────────────────────────────────────────────────────────────────┐
|
||||
│ SEMANTICA FRAMEWORK │
|
||||
├─────────────────────────────────────────────────────────────────────┤
|
||||
│ │
|
||||
│ ┌──────────────────────────────────────────────────────────────┐ │
|
||||
│ │ DATA INGESTION LAYER │ │
|
||||
│ │ ┌────────┬────────┬────────┬────────┬────────┬──────────┐ │ │
|
||||
│ │ │ Files │ Web │ Feeds │ APIs │Streams │ Archives │ │ │
|
||||
│ │ └────────┴────────┴────────┴────────┴────────┴──────────┘ │ │
|
||||
│ │ 50+ Formats • Real-time • Multi-modal │ │
|
||||
│ └──────────────────────────────────────────────────────────────┘ │
|
||||
│ ↓ │
|
||||
│ ┌──────────────────────────────────────────────────────────────┐ │
|
||||
│ │ SEMANTIC PROCESSING LAYER │ │
|
||||
│ │ ┌──────────┬────────────┬────────────┬──────────────────┐ │ │
|
||||
│ │ │ Parse │ Normalize │ Extract │ Build Graph │ │ │
|
||||
│ │ │ │ │ Semantics │ │ │ │
|
||||
│ │ └──────────┴────────────┴────────────┴──────────────────┘ │ │
|
||||
│ │ NLP • Embeddings • Ontologies • Quality Assurance │ │
|
||||
│ └──────────────────────────────────────────────────────────────┘ │
|
||||
│ ↓ │
|
||||
│ ┌──────────────────────────────────────────────────────────────┐ │
|
||||
│ │ APPLICATION LAYER │ │
|
||||
│ │ ┌──────────┬────────────┬────────────┬──────────────────┐ │ │
|
||||
│ │ │ GraphRAG │ AI Agents │Multi-Agent │ Analytics │ │ │
|
||||
│ │ │ │ │ Systems │ Copilots │ │ │
|
||||
│ │ └──────────┴────────────┴────────────┴──────────────────┘ │ │
|
||||
│ │ Hybrid Retrieval • Context Engineering • Reasoning │ │
|
||||
│ └──────────────────────────────────────────────────────────────┘ │
|
||||
│ │
|
||||
└─────────────────────────────────────────────────────────────────────┘
|
||||
</pre>
|
||||
<p class="caption">Figure 1: High-level system architecture showing three main layers</p>
|
||||
</div>
|
||||
|
||||
<h2>2. Data Ingestion Pipeline Flow</h2>
|
||||
<div class="diagram">
|
||||
<pre>
|
||||
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
|
||||
│ Files │ │ Web │ │ Feeds │
|
||||
│ (50+ fmt) │ │ Scraping │ │ RSS/Atom │
|
||||
└──────┬──────┘ └──────┬──────┘ └──────┬──────┘
|
||||
│ │ │
|
||||
└───────────────────┼───────────────────┘
|
||||
│
|
||||
┌────────▼────────┐
|
||||
│ FileIngestor │
|
||||
│ WebIngestor │
|
||||
│ FeedIngestor │
|
||||
│ DBIngestor │
|
||||
│ StreamIngestor │
|
||||
└────────┬────────┘
|
||||
│
|
||||
┌────────▼────────┐
|
||||
│ Source Objects │
|
||||
│ (with metadata) │
|
||||
└────────┬────────┘
|
||||
│
|
||||
┌────────▼────────┐
|
||||
│ Parse Layer │
|
||||
│ (DocumentParser)│
|
||||
└─────────────────┘
|
||||
</pre>
|
||||
<p class="caption">Figure 2: Data ingestion pipeline supporting multiple input sources</p>
|
||||
</div>
|
||||
|
||||
<h2>3. Semantic Processing Pipeline</h2>
|
||||
<div class="diagram">
|
||||
<pre>
|
||||
Raw Text/Data
|
||||
│
|
||||
├─→ [Parse] ──→ Structured Content
|
||||
│
|
||||
├─→ [Normalize] ──→ Cleaned Text
|
||||
│
|
||||
├─→ [Split] ──→ Chunks
|
||||
│
|
||||
├─→ [Embed] ──→ Vector Embeddings ──→ Vector Store
|
||||
│
|
||||
└─→ [Extract Semantics]
|
||||
│
|
||||
├─→ Entities ──→ Entity Store
|
||||
│
|
||||
├─→ Relations ──→ Relation Store
|
||||
│
|
||||
├─→ Events ──→ Event Store
|
||||
│
|
||||
└─→ Triples ──→ Triple Store
|
||||
│
|
||||
└─→ [Build Graph] ──→ Knowledge Graph
|
||||
│
|
||||
├─→ Entity Resolution
|
||||
├─→ Conflict Detection
|
||||
├─→ Deduplication
|
||||
└─→ Quality Assurance
|
||||
</pre>
|
||||
<p class="caption">Figure 3: Complete semantic processing pipeline from raw data to knowledge graph</p>
|
||||
</div>
|
||||
|
||||
<h2>4. Knowledge Graph Construction Flow</h2>
|
||||
<div class="diagram">
|
||||
<pre>
|
||||
Multiple Sources
|
||||
│
|
||||
├─→ Source 1 ──→ Entities + Relations
|
||||
├─→ Source 2 ──→ Entities + Relations
|
||||
└─→ Source N ──→ Entities + Relations
|
||||
│
|
||||
▼
|
||||
┌────────────────┐
|
||||
│ Entity Pool │
|
||||
└────────┬───────┘
|
||||
│
|
||||
▼
|
||||
┌────────────────┐
|
||||
│ Entity │
|
||||
│ Resolution │──→ Merge Duplicates
|
||||
└────────┬───────┘
|
||||
│
|
||||
▼
|
||||
┌────────────────┐
|
||||
│ Conflict │
|
||||
│ Detection │──→ Resolve Conflicts
|
||||
└────────┬───────┘
|
||||
│
|
||||
▼
|
||||
┌────────────────┐
|
||||
│ Graph Builder │
|
||||
│ │
|
||||
│ • Add Nodes │
|
||||
│ • Add Edges │
|
||||
│ • Add Metadata│
|
||||
└────────┬───────┘
|
||||
│
|
||||
▼
|
||||
┌────────────────┐
|
||||
│ Knowledge Graph│
|
||||
│ │
|
||||
│ • Nodes │
|
||||
│ • Edges │
|
||||
│ • Properties │
|
||||
└────────────────┘
|
||||
</pre>
|
||||
<p class="caption">Figure 4: Knowledge graph construction with entity resolution and conflict handling</p>
|
||||
</div>
|
||||
|
||||
<h2>5. GraphRAG Query Flow</h2>
|
||||
<div class="diagram">
|
||||
<pre>
|
||||
User Query
|
||||
│
|
||||
├─→ [Vector Search] ──→ Top-K Similar Chunks
|
||||
│ │
|
||||
│ ├─→ Extract Entities
|
||||
│ │
|
||||
│ └─→ Seed Entities
|
||||
│ │
|
||||
│ ▼
|
||||
│ ┌──────────────┐
|
||||
│ │ Graph │
|
||||
│ │ Expansion │
|
||||
│ │ (2-3 hops) │
|
||||
│ └──────┬───────┘
|
||||
│ │
|
||||
│ ▼
|
||||
│ Expanded Context
|
||||
│ │
|
||||
└─→ [Graph Traversal] ──────────────┘
|
||||
│
|
||||
▼
|
||||
┌────────────────┐
|
||||
│ Hybrid │
|
||||
│ Retrieval │──→ Rerank Results
|
||||
│ (Vector + Graph)│
|
||||
└────────┬───────┘
|
||||
│
|
||||
▼
|
||||
┌────────────────┐
|
||||
│ Context │
|
||||
│ Building │──→ Structured Context
|
||||
└────────┬───────┘
|
||||
│
|
||||
▼
|
||||
┌────────────────┐
|
||||
│ LLM Generation │──→ Final Answer
|
||||
└────────────────┘
|
||||
</pre>
|
||||
<p class="caption">Figure 5: GraphRAG query flow combining vector search with graph traversal</p>
|
||||
</div>
|
||||
|
||||
<h2>6. Component Interaction Diagram</h2>
|
||||
<div class="diagram">
|
||||
<pre>
|
||||
┌──────────────┐
|
||||
│ Semantica │
|
||||
│ (Core) │
|
||||
└──────┬───────┘
|
||||
│
|
||||
├─→ Orchestrator
|
||||
│ │
|
||||
│ ├─→ ConfigManager
|
||||
│ ├─→ PluginRegistry
|
||||
│ └─→ LifecycleManager
|
||||
│
|
||||
├─→ Pipeline
|
||||
│ │
|
||||
│ ├─→ PipelineBuilder
|
||||
│ ├─→ ExecutionEngine
|
||||
│ └─→ ParallelismManager
|
||||
│
|
||||
├─→ Ingest Layer
|
||||
│ │
|
||||
│ ├─→ FileIngestor
|
||||
│ ├─→ WebIngestor
|
||||
│ └─→ DBIngestor
|
||||
│
|
||||
├─→ Parse Layer
|
||||
│ │
|
||||
│ ├─→ DocumentParser
|
||||
│ ├─→ WebParser
|
||||
│ └─→ StructuredDataParser
|
||||
│
|
||||
├─→ Semantic Extract
|
||||
│ │
|
||||
│ ├─→ NamedEntityRecognizer
|
||||
│ ├─→ RelationExtractor
|
||||
│ └─→ TripleExtractor
|
||||
│
|
||||
├─→ Knowledge Graph
|
||||
│ │
|
||||
│ ├─→ GraphBuilder
|
||||
│ ├─→ EntityResolver
|
||||
│ └─→ GraphAnalyzer
|
||||
│
|
||||
└─→ Application Layer
|
||||
│
|
||||
├─→ GraphRAGEngine
|
||||
├─→ ContextBuilder
|
||||
└─→ ReasoningEngine
|
||||
</pre>
|
||||
<p class="caption">Figure 6: Component interaction and module organization</p>
|
||||
</div>
|
||||
|
||||
<h2>7. Deployment Architecture - Single Node</h2>
|
||||
<div class="diagram">
|
||||
<pre>
|
||||
┌─────────────────────────────────────┐
|
||||
│ Application Server │
|
||||
│ │
|
||||
│ ┌───────────────────────────────┐ │
|
||||
│ │ Semantica Framework │ │
|
||||
│ │ • Orchestrator │ │
|
||||
│ │ • Processing Modules │ │
|
||||
│ │ • API Endpoints │ │
|
||||
│ └───────────────────────────────┘ │
|
||||
│ │
|
||||
│ ┌───────────────────────────────┐ │
|
||||
│ │ Local Storage │ │
|
||||
│ │ • FAISS (Vectors) │ │
|
||||
│ │ • NetworkX (Graph) │ │
|
||||
│ │ • File System (Cache) │ │
|
||||
│ └───────────────────────────────┘ │
|
||||
└─────────────────────────────────────┘
|
||||
</pre>
|
||||
<p class="caption">Figure 7: Single node deployment for small to medium scale</p>
|
||||
</div>
|
||||
|
||||
<h2>8. Deployment Architecture - Distributed</h2>
|
||||
<div class="diagram">
|
||||
<pre>
|
||||
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
|
||||
│ Worker 1 │ │ Worker 2 │ │ Worker N │
|
||||
│ (Ingestion) │ │ (Processing) │ │ (Graph) │
|
||||
│ │ │ │ │ │
|
||||
│ • FileIngest │ │ • NER │ │ • GraphBuild │
|
||||
│ • WebIngest │ │ • Relations │ │ • Analytics │
|
||||
│ • Parse │ │ • Embeddings │ │ • Queries │
|
||||
└──────┬───────┘ └──────┬───────┘ └──────┬───────┘
|
||||
│ │ │
|
||||
└─────────────────┼─────────────────┘
|
||||
│
|
||||
┌──────────▼──────────┐
|
||||
│ Message Queue │
|
||||
│ (Kafka/RabbitMQ) │
|
||||
│ • Task Queue │
|
||||
│ • Event Stream │
|
||||
└──────────┬──────────┘
|
||||
│
|
||||
┌─────────────────┼─────────────────┐
|
||||
│ │ │
|
||||
┌──────▼──────┐ ┌──────▼──────┐ ┌──────▼──────┐
|
||||
│ Neo4j │ │ Vector DB │ │ Triple │
|
||||
│ (Graph) │ │ (Pinecone) │ │ Store │
|
||||
│ │ │ │ │ (Virtuoso) │
|
||||
└─────────────┘ └──────────────┘ └─────────────┘
|
||||
</pre>
|
||||
<p class="caption">Figure 8: Distributed deployment for enterprise scale</p>
|
||||
</div>
|
||||
|
||||
<h2>9. 6-Stage Ontology Generation Pipeline</h2>
|
||||
<div class="diagram">
|
||||
<pre>
|
||||
Unstructured Content
|
||||
│
|
||||
▼
|
||||
┌─────────────────────┐
|
||||
│ Stage 1: Semantic │
|
||||
│ Network Parsing │──→ Extract domain concepts
|
||||
└──────────┬──────────┘
|
||||
│
|
||||
▼
|
||||
┌─────────────────────┐
|
||||
│ Stage 2: YAML-to- │
|
||||
│ Definition │──→ Transform to class definitions
|
||||
└──────────┬──────────┘
|
||||
│
|
||||
▼
|
||||
┌─────────────────────┐
|
||||
│ Stage 3: Definition│
|
||||
│ to Types │──→ Map to OWL types
|
||||
└──────────┬──────────┘
|
||||
│
|
||||
▼
|
||||
┌─────────────────────┐
|
||||
│ Stage 4: Hierarchy │
|
||||
│ Generation │──→ Build taxonomic structures
|
||||
└──────────┬──────────┘
|
||||
│
|
||||
▼
|
||||
┌─────────────────────┐
|
||||
│ Stage 5: TTL │
|
||||
│ Generation │──→ Generate OWL/Turtle syntax
|
||||
└──────────┬──────────┘
|
||||
│
|
||||
▼
|
||||
┌─────────────────────┐
|
||||
│ Stage 6: Symbolic │
|
||||
│ Validation │──→ HermiT/Pellet reasoning
|
||||
│ │ (F1 up to 0.99)
|
||||
└──────────┬──────────┘
|
||||
│
|
||||
▼
|
||||
W3C-Compliant
|
||||
OWL Ontology
|
||||
</pre>
|
||||
<p class="caption">Figure 9: Automatic ontology generation pipeline with symbolic validation</p>
|
||||
</div>
|
||||
|
||||
<h2>10. Real-Time Streaming Architecture (Future)</h2>
|
||||
<div class="diagram">
|
||||
<pre>
|
||||
Data Streams
|
||||
│
|
||||
├─→ Kafka Topic ──→ Stream Processor
|
||||
├─→ RabbitMQ ──→ Stream Processor
|
||||
└─→ Kinesis ──→ Stream Processor
|
||||
│
|
||||
▼
|
||||
┌────────────────┐
|
||||
│ Event Handler │
|
||||
│ • Parse │
|
||||
│ • Extract │
|
||||
│ • Validate │
|
||||
└────────┬───────┘
|
||||
│
|
||||
▼
|
||||
┌────────────────┐
|
||||
│ Incremental │
|
||||
│ Graph Update │
|
||||
│ • Add Entities │
|
||||
│ • Add Relations│
|
||||
│ • Resolve │
|
||||
└────────┬───────┘
|
||||
│
|
||||
▼
|
||||
┌────────────────┐
|
||||
│ Graph Store │
|
||||
│ (Versioned) │
|
||||
└────────────────┘
|
||||
</pre>
|
||||
<p class="caption">Figure 10: Real-time streaming architecture for incremental knowledge graph updates</p>
|
||||
</div>
|
||||
|
||||
<hr>
|
||||
<p><em>Document Version: 1.0 | Semantica Framework Architecture Diagrams</em></p>
|
||||
</body>
|
||||
</html>
|
||||
|
||||
@@ -0,0 +1,247 @@
|
||||
SEMANTICA GRANT APPLICATION - DETAILED BUDGET BREAKDOWN
|
||||
========================================================
|
||||
|
||||
Requested Amount: €44,000
|
||||
Project Duration: 12 months (full project timeline)
|
||||
Grant Period: 12 months
|
||||
|
||||
The requested budget will be used to support 12 months of focused development,
|
||||
contributor onboarding, mentorship, community workshops, and essential
|
||||
infrastructure. Semantica is a fully bootstrapped open-source project with no
|
||||
past or current funding sources.
|
||||
|
||||
================================================================================
|
||||
1. HUMAN LABOR (€22,000 - 50% of total budget)
|
||||
================================================================================
|
||||
|
||||
1.1 Lead Developer (Full-Time) - €18,000
|
||||
- Duration: 12 months
|
||||
- Rate: €1,500/month
|
||||
- Main Tasks:
|
||||
* Multi-language semantic extraction and cross-lingual graph generation
|
||||
* Real-time streaming knowledge graph updates
|
||||
* Multi-modal semantic extraction foundation (text-vision-audio)
|
||||
* Distributed processing and performance optimization
|
||||
* Automated QA improvements (conflict detection, schema validation)
|
||||
* Integrations (LangChain, Haystack, Neo4j/Stardog)
|
||||
* Mentorship and training of new contributors
|
||||
|
||||
- Quarterly Allocation:
|
||||
* Q1 (Months 1-3): Real-time streaming + scalability (€4,500)
|
||||
* Q2 (Months 4-6): Multi-modal processing + automated QA (€4,500)
|
||||
* Q3 (Months 7-9): Integration + testing + optimization (€4,500)
|
||||
* Q4 (Months 10-12): Production hardening + documentation (€4,500)
|
||||
|
||||
1.2 Contributor Support - €4,000
|
||||
- Contributor Onboarding: €1,000
|
||||
* Documentation setup: €300
|
||||
* Mentoring sessions: €500
|
||||
* Initial training and tooling: €200
|
||||
|
||||
- Part-time Contributor Stipends: €3,000
|
||||
* 2-3 contributors @ €500-1,000/month for 2-3 months
|
||||
* Focus areas:
|
||||
- Documentation and tutorials
|
||||
- Test coverage expansion
|
||||
- Module development
|
||||
- Ontology/graph improvements
|
||||
|
||||
================================================================================
|
||||
2. INFRASTRUCTURE & TOOLS (€12,000 - 27% of total budget)
|
||||
================================================================================
|
||||
|
||||
2.1 Database Hosting and Services: €3,500
|
||||
- Neo4j cloud hosting for knowledge graphs: €1,400
|
||||
- PostgreSQL databases for metadata storage: €1,000
|
||||
- Vector database services (Pinecone/Weaviate): €700
|
||||
- Triple store hosting (Virtuoso/Stardog): €400
|
||||
|
||||
2.2 Cloud CI/CD Pipelines and Testing Environments: €3,000
|
||||
- AWS/GCP credits for automated testing: €1,500
|
||||
- CI/CD pipeline infrastructure: €1,000
|
||||
- Testing environments and contributor access: €500
|
||||
|
||||
2.3 GPU Compute for Embedding + Multi-Modal Experiments: €2,500
|
||||
- Cloud GPU access for embedding model testing: €1,500
|
||||
- Multi-modal model training and fine-tuning: €1,000
|
||||
|
||||
2.4 Developer Tools, Licenses, Security Scanning: €1,000
|
||||
- IDE licenses (PyCharm/VS Code): €300
|
||||
- Monitoring tools (Sentry, DataDog): €400
|
||||
- Security scanning tools: €300
|
||||
|
||||
2.5 Storage, Monitoring, Backups: €1,000
|
||||
- Cloud storage for artifacts and backups: €600
|
||||
- Monitoring and alerting services: €400
|
||||
|
||||
2.6 API Services and External Tool Integrations: €1,000
|
||||
- LLM API costs (OpenAI, Anthropic) for ontology generation: €600
|
||||
- External API integrations and webhooks: €400
|
||||
|
||||
================================================================================
|
||||
3. COMMUNITY, WORKSHOPS & TRAVEL (€4,500 - 10% of total budget)
|
||||
================================================================================
|
||||
|
||||
3.1 Presenting Semantica at AI/ML Meetups and Conferences: €2,000
|
||||
- Conference registration fees: €1,000
|
||||
- Travel expenses: €700
|
||||
- Accommodation: €300
|
||||
|
||||
3.2 Workshops for Contributors and Users: €1,500
|
||||
- Virtual and in-person workshops:
|
||||
* Hands-on tutorials for ontology generation
|
||||
* Knowledge graph design workshops
|
||||
* Semantica module development bootcamps
|
||||
* Community documentation sprints
|
||||
* Live Q&A sessions
|
||||
- Workshop materials and platform costs: €800
|
||||
- Travel for in-person workshops: €700
|
||||
|
||||
3.3 Community Engagement Activities: €1,000
|
||||
- Community events and meetups: €500
|
||||
- Online community platform costs: €300
|
||||
- Community swag and recognition: €200
|
||||
|
||||
================================================================================
|
||||
4. RESEARCH & BENCHMARKING (€3,000 - 7% of total budget)
|
||||
================================================================================
|
||||
|
||||
4.1 Datasets and Evaluation Environments: €1,500
|
||||
- Domain-specific datasets for testing: €800
|
||||
- Evaluation framework setup: €500
|
||||
- Benchmark dataset acquisition: €200
|
||||
|
||||
4.2 Ontology/GraphRAG Benchmarks: €1,000
|
||||
- Benchmark development and execution: €600
|
||||
- Performance comparison studies: €400
|
||||
|
||||
4.3 Multi-Modal Research and Experimentation: €500
|
||||
- Multi-modal dataset acquisition: €300
|
||||
- Cross-modal alignment research: €200
|
||||
|
||||
================================================================================
|
||||
5. OPERATIONAL & ADMINISTRATIVE COSTS (€1,500 - 3% of total budget)
|
||||
================================================================================
|
||||
|
||||
5.1 Accounting and Compliance: €600
|
||||
- Financial management and reporting: €400
|
||||
- Tax compliance and documentation: €200
|
||||
|
||||
5.2 Domains, Hosting, Bandwidth, Misc. Services: €900
|
||||
- Domain registration and hosting: €300
|
||||
- Bandwidth and CDN services: €400
|
||||
- Miscellaneous administrative services: €200
|
||||
|
||||
================================================================================
|
||||
6. CONTINGENCY (€2,000 - 5% of total budget)
|
||||
================================================================================
|
||||
|
||||
- Reserved for unforeseen technical challenges: €1,000
|
||||
- Community needs and additional contributor support: €600
|
||||
- Scope adjustments and unexpected costs: €400
|
||||
|
||||
================================================================================
|
||||
QUARTERLY BREAKDOWN (12 Months)
|
||||
================================================================================
|
||||
|
||||
Q1 (Months 1-3):
|
||||
- Lead Development: €4,500
|
||||
- Database/Infrastructure: €3,000
|
||||
- Contributor Onboarding: €1,000
|
||||
- Community: €500
|
||||
- Total Q1: €9,000
|
||||
|
||||
Q2 (Months 4-6):
|
||||
- Lead Development: €4,500
|
||||
- Database/Infrastructure: €3,000
|
||||
- Contributor Stipends: €3,000
|
||||
- Research: €800
|
||||
- Travel: €1,000
|
||||
- Total Q2: €12,300
|
||||
|
||||
Q3 (Months 7-9):
|
||||
- Lead Development: €4,500
|
||||
- Database/Infrastructure: €3,000
|
||||
- Workshops: €1,000
|
||||
- Research: €1,200
|
||||
- Operational: €500
|
||||
- Total Q3: €10,200
|
||||
|
||||
Q4 (Months 10-12):
|
||||
- Lead Development: €4,500
|
||||
- Database/Infrastructure: €3,000
|
||||
- Community: €1,000
|
||||
- Travel: €1,000
|
||||
- Operational: €1,000
|
||||
- Contingency: €2,000
|
||||
- Total Q4: €12,500
|
||||
|
||||
Total: €9,000 + €12,300 + €10,200 + €12,500 = €44,000
|
||||
|
||||
================================================================================
|
||||
COST BREAKDOWN BY CATEGORY
|
||||
================================================================================
|
||||
|
||||
Human Labor: €22,000 (50%)
|
||||
Infrastructure & Tools: €12,000 (27%)
|
||||
Community & Travel: €4,500 (10%)
|
||||
Research & Benchmarking: €3,000 (7%)
|
||||
Contingency: €2,000 (5%)
|
||||
Operational: €1,500 (3%)
|
||||
----------------------------------------
|
||||
TOTAL: €44,000 (100%)
|
||||
|
||||
================================================================================
|
||||
RATES JUSTIFICATION
|
||||
================================================================================
|
||||
|
||||
Lead Developer (€1,500/month for 12 months):
|
||||
- Full-time equivalent development work
|
||||
- Includes: coding, architecture, testing, code review, mentorship
|
||||
- Market rate for senior AI engineer: €5,000-7,000/month
|
||||
- Adjusted for grant context: €1,500/month enables sustainable 12-month
|
||||
development while maintaining focus on open-source contribution
|
||||
|
||||
Part-time Contributors (€500-1,000/month):
|
||||
- 10-20 hours/month @ €50/hour
|
||||
- Focused contributions on specific areas
|
||||
- Competitive with open-source contributor stipends
|
||||
- Duration: 2-3 months per contributor
|
||||
|
||||
================================================================================
|
||||
OTHER FUNDING SOURCES
|
||||
================================================================================
|
||||
|
||||
None. Semantica is a fully bootstrapped open-source project developed solo.
|
||||
Having quit my job as an AI Engineer to focus on this project, the grant is
|
||||
essential to sustain full-time development, expand the contributor base through
|
||||
mentorship and workshops, and bring Semantica to production maturity.
|
||||
|
||||
================================================================================
|
||||
BUDGET ALLOCATION RATIONALE
|
||||
================================================================================
|
||||
|
||||
1. Human Labor (64%): Core development work and contributor support to build
|
||||
sustainable open-source ecosystem.
|
||||
|
||||
2. Infrastructure & Tools (16%): Essential for testing, CI/CD, GPU compute,
|
||||
and multi-modal experiments.
|
||||
|
||||
3. Community & Travel (10%): Critical for growing the open-source community
|
||||
through workshops, conferences, and mentorship.
|
||||
|
||||
4. Research & Benchmarking (5%): Ensures quality and validates improvements
|
||||
through proper evaluation.
|
||||
|
||||
5. Contingency (5%): Standard practice for unforeseen challenges in technical
|
||||
projects.
|
||||
|
||||
6. Operational (3%): Necessary administrative and infrastructure costs.
|
||||
|
||||
This budget supports a sustainable 12-month development cycle, improved
|
||||
contributor experience, open workshops for the wider community, and a strong
|
||||
mentorship component to grow Semantica's open-source ecosystem.
|
||||
|
||||
================================================================================
|
||||
END OF BUDGET BREAKDOWN
|
||||
================================================================================
|
||||
@@ -0,0 +1,340 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<title>Semantica - Community Engagement Plan</title>
|
||||
<style>
|
||||
body { font-family: Arial, sans-serif; line-height: 1.6; max-width: 1200px; margin: 0 auto; padding: 20px; }
|
||||
h1 { color: #2c3e50; border-bottom: 3px solid #3498db; padding-bottom: 10px; }
|
||||
h2 { color: #34495e; margin-top: 30px; border-bottom: 2px solid #ecf0f1; padding-bottom: 5px; }
|
||||
h3 { color: #555; margin-top: 20px; }
|
||||
table { border-collapse: collapse; width: 100%; margin: 20px 0; }
|
||||
th, td { border: 1px solid #ddd; padding: 12px; text-align: left; }
|
||||
th { background-color: #3498db; color: white; }
|
||||
tr:nth-child(even) { background-color: #f2f2f2; }
|
||||
.strategy { background-color: #f9f9f9; padding: 15px; margin: 15px 0; border-left: 4px solid #3498db; }
|
||||
.metric { background-color: #e8f5e9; padding: 10px; margin: 10px 0; border-left: 4px solid #4caf50; }
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<h1>Semantica Framework - Community Engagement Plan</h1>
|
||||
|
||||
<h2>1. Community Building Strategy</h2>
|
||||
|
||||
<div class="strategy">
|
||||
<h3>1.1 GitHub Community</h3>
|
||||
<ul>
|
||||
<li><strong>Active Repository:</strong> Maintain responsive issue tracking and pull request reviews</li>
|
||||
<li><strong>Contribution Guidelines:</strong> Clear CONTRIBUTING.md with code of conduct</li>
|
||||
<li><strong>Good First Issues:</strong> Label beginner-friendly issues to onboard new contributors</li>
|
||||
<li><strong>Release Notes:</strong> Detailed changelog for each release</li>
|
||||
<li><strong>Discussions:</strong> Use GitHub Discussions for Q&A and feature requests</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<div class="strategy">
|
||||
<h3>1.2 Discord Community</h3>
|
||||
<ul>
|
||||
<li><strong>Real-Time Support:</strong> Active Discord server for immediate help</li>
|
||||
<li><strong>Channels:</strong> Separate channels for general, technical, showcase, and announcements</li>
|
||||
<li><strong>Office Hours:</strong> Weekly office hours for direct developer interaction</li>
|
||||
<li><strong>Community Showcases:</strong> Regular showcases of community projects</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<div class="strategy">
|
||||
<h3>1.3 Content Creation</h3>
|
||||
<ul>
|
||||
<li><strong>Blog Posts:</strong> Monthly technical articles on semantic engineering</li>
|
||||
<li><strong>YouTube Tutorials:</strong> Video series covering use cases and advanced features</li>
|
||||
<li><strong>Technical Articles:</strong> Deep dives into architecture and design decisions</li>
|
||||
<li><strong>Case Studies:</strong> Real-world implementation stories</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<h2>2. Documentation Roadmap</h2>
|
||||
|
||||
<table>
|
||||
<tr>
|
||||
<th>Timeline</th>
|
||||
<th>Documentation Type</th>
|
||||
<th>Deliverable</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Month 1-2</td>
|
||||
<td>API Documentation</td>
|
||||
<td>Complete API reference for all 29 modules</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Month 3-4</td>
|
||||
<td>Tutorial Series</td>
|
||||
<td>10+ step-by-step tutorials covering common use cases</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Month 5-6</td>
|
||||
<td>Cookbook Expansion</td>
|
||||
<td>Expand from 50+ to 75+ Jupyter notebooks</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Month 7-8</td>
|
||||
<td>Best Practices Guide</td>
|
||||
<td>Production deployment and optimization guide</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Month 9-10</td>
|
||||
<td>Video Tutorials</td>
|
||||
<td>20+ video tutorials for complex workflows</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Month 11-12</td>
|
||||
<td>Migration Guides</td>
|
||||
<td>Guides for migrating from other tools</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<h2>3. Tutorial and Example Creation Plan</h2>
|
||||
|
||||
<h3>3.1 Beginner Tutorials</h3>
|
||||
<ul>
|
||||
<li>Getting Started with Semantica (5 minutes)</li>
|
||||
<li>Building Your First Knowledge Graph (15 minutes)</li>
|
||||
<li>Basic Entity Extraction (10 minutes)</li>
|
||||
<li>Simple GraphRAG Setup (20 minutes)</li>
|
||||
</ul>
|
||||
|
||||
<h3>3.2 Intermediate Tutorials</h3>
|
||||
<ul>
|
||||
<li>Multi-Source Knowledge Graph Construction</li>
|
||||
<li>Automatic Ontology Generation</li>
|
||||
<li>Conflict Resolution Strategies</li>
|
||||
<li>Custom Entity Extractors</li>
|
||||
<li>Graph Analytics and Visualization</li>
|
||||
</ul>
|
||||
|
||||
<h3>3.3 Advanced Tutorials</h3>
|
||||
<ul>
|
||||
<li>Production Deployment Architecture</li>
|
||||
<li>Distributed Processing Setup</li>
|
||||
<li>Custom Reasoning Rules</li>
|
||||
<li>Performance Optimization</li>
|
||||
<li>Federated Knowledge Graphs</li>
|
||||
</ul>
|
||||
|
||||
<h3>3.4 Domain-Specific Examples</h3>
|
||||
<ul>
|
||||
<li>Healthcare: Medical knowledge graph from clinical notes</li>
|
||||
<li>Finance: Financial entity extraction and relationship mapping</li>
|
||||
<li>Legal: Contract analysis and knowledge extraction</li>
|
||||
<li>Research: Scientific paper knowledge graph construction</li>
|
||||
<li>Enterprise: Internal documentation knowledge base</li>
|
||||
</ul>
|
||||
|
||||
<h2>4. Conference Presentation Schedule</h2>
|
||||
|
||||
<table>
|
||||
<tr>
|
||||
<th>Quarter</th>
|
||||
<th>Event Type</th>
|
||||
<th>Target Events</th>
|
||||
<th>Content Focus</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Q1 2025</td>
|
||||
<td>AI/ML Conferences</td>
|
||||
<td>PyData, AI Engineering Summit</td>
|
||||
<td>Introduction to Semantica, use cases</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Q2 2025</td>
|
||||
<td>Semantic Web</td>
|
||||
<td>ISWC, ESWC</td>
|
||||
<td>Ontology generation, RDF/OWL support</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Q3 2025</td>
|
||||
<td>Open Source</td>
|
||||
<td>OSCON, FOSDEM</td>
|
||||
<td>Open-source commitment, community</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Q4 2025</td>
|
||||
<td>Industry</td>
|
||||
<td>Enterprise AI Summit</td>
|
||||
<td>Production deployments, case studies</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<h2>5. Contributor Onboarding Process</h2>
|
||||
|
||||
<div class="strategy">
|
||||
<h3>5.1 Onboarding Steps</h3>
|
||||
<ol>
|
||||
<li><strong>Initial Contact:</strong> Contributor expresses interest via GitHub/Discord</li>
|
||||
<li><strong>Welcome Package:</strong> Send contributor guide, code of conduct, setup instructions</li>
|
||||
<li><strong>Mentorship Assignment:</strong> Pair with experienced contributor or maintainer</li>
|
||||
<li><strong>First Contribution:</strong> Guide through "good first issue"</li>
|
||||
<li><strong>Code Review:</strong> Provide constructive feedback and learning opportunities</li>
|
||||
<li><strong>Recognition:</strong> Acknowledge contributions in release notes and contributors file</li>
|
||||
</ol>
|
||||
</div>
|
||||
|
||||
<div class="strategy">
|
||||
<h3>5.2 Contributor Support</h3>
|
||||
<ul>
|
||||
<li><strong>Documentation:</strong> Comprehensive contributor documentation</li>
|
||||
<li><strong>Office Hours:</strong> Weekly sessions for questions and guidance</li>
|
||||
<li><strong>Stipends:</strong> Financial support for significant contributions (via grant funding)</li>
|
||||
<li><strong>Recognition:</strong> Contributor badges, featured showcases, swag</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<h2>6. Integration and Interoperability</h2>
|
||||
|
||||
<h3>6.1 Planned Integrations</h3>
|
||||
<table>
|
||||
<tr>
|
||||
<th>Tool/Framework</th>
|
||||
<th>Integration Type</th>
|
||||
<th>Timeline</th>
|
||||
<th>Priority</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>LangChain</td>
|
||||
<td>Tool/Agent Integration</td>
|
||||
<td>Month 2-3</td>
|
||||
<td>High</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Haystack</td>
|
||||
<td>RAG Pipeline</td>
|
||||
<td>Month 3-4</td>
|
||||
<td>High</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>LlamaIndex</td>
|
||||
<td>RAG Enhancement</td>
|
||||
<td>Month 4-5</td>
|
||||
<td>Medium</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Neo4j</td>
|
||||
<td>Graph Storage Connector</td>
|
||||
<td>Month 1-2</td>
|
||||
<td>High</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Stardog</td>
|
||||
<td>RDF/OWL Storage</td>
|
||||
<td>Month 5-6</td>
|
||||
<td>Medium</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<h2>7. Adoption Support Strategy</h2>
|
||||
|
||||
<div class="strategy">
|
||||
<h3>7.1 Example Projects</h3>
|
||||
<ul>
|
||||
<li>Reference implementations for common use cases</li>
|
||||
<li>Starter templates for different domains</li>
|
||||
<li>Production-ready examples with best practices</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<div class="strategy">
|
||||
<h3>7.2 Migration Guides</h3>
|
||||
<ul>
|
||||
<li>Migrating from manual knowledge engineering</li>
|
||||
<li>Migrating from other graph construction tools</li>
|
||||
<li>Integrating with existing RAG systems</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<div class="strategy">
|
||||
<h3>7.3 Performance Benchmarks</h3>
|
||||
<ul>
|
||||
<li>Published benchmarks comparing with alternatives</li>
|
||||
<li>Performance optimization guides</li>
|
||||
<li>Scalability studies and case studies</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<h2>8. Success Metrics and KPIs</h2>
|
||||
|
||||
<div class="metric">
|
||||
<h3>8.1 Community Growth Metrics</h3>
|
||||
<ul>
|
||||
<li><strong>GitHub Stars:</strong> Target 1,000+ by end of year</li>
|
||||
<li><strong>Contributors:</strong> Target 20+ active contributors</li>
|
||||
<li><strong>Forks:</strong> Target 200+ forks</li>
|
||||
<li><strong>Issues/PRs:</strong> Maintain healthy issue-to-PR ratio (2:1)</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<div class="metric">
|
||||
<h3>8.2 Adoption Metrics</h3>
|
||||
<ul>
|
||||
<li><strong>PyPI Downloads:</strong> Target 10,000+ monthly downloads</li>
|
||||
<li><strong>Production Deployments:</strong> Track via case studies and testimonials</li>
|
||||
<li><strong>Community Projects:</strong> 10+ community-built projects using Semantica</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<div class="metric">
|
||||
<h3>8.3 Engagement Metrics</h3>
|
||||
<ul>
|
||||
<li><strong>Discord Members:</strong> Target 500+ active members</li>
|
||||
<li><strong>GitHub Discussions:</strong> 50+ active discussions</li>
|
||||
<li><strong>Blog Views:</strong> 5,000+ monthly views</li>
|
||||
<li><strong>YouTube Views:</strong> 10,000+ total views</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<div class="metric">
|
||||
<h3>8.4 Quality Metrics</h3>
|
||||
<ul>
|
||||
<li><strong>Test Coverage:</strong> Maintain 80%+ code coverage</li>
|
||||
<li><strong>Documentation Coverage:</strong> 100% API documentation</li>
|
||||
<li><strong>Response Time:</strong> Average 24-hour response to issues</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<h2>9. Open-Source Commitment</h2>
|
||||
|
||||
<div class="strategy">
|
||||
<h3>9.1 License and Standards</h3>
|
||||
<ul>
|
||||
<li><strong>License:</strong> MIT License (maximum adoption)</li>
|
||||
<li><strong>Open Standards:</strong> Full RDF, OWL, SPARQL, JSON-LD compliance</li>
|
||||
<li><strong>Transparency:</strong> Public roadmap, open development process</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<div class="strategy">
|
||||
<h3>9.2 Community Governance</h3>
|
||||
<ul>
|
||||
<li><strong>Code of Conduct:</strong> Contributor Covenant</li>
|
||||
<li><strong>Decision Making:</strong> Community-driven feature prioritization</li>
|
||||
<li><strong>Maintainer Guidelines:</strong> Clear process for becoming a maintainer</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<h2>10. Long-Term Vision</h2>
|
||||
|
||||
<p>Establish Semantica as the <strong>de-facto open-source semantic layer framework</strong>, enabling a new generation of context-aware AI applications. Through:</p>
|
||||
<ul>
|
||||
<li>Comprehensive open-source development</li>
|
||||
<li>Active community engagement and support</li>
|
||||
<li>High-quality documentation and tutorials</li>
|
||||
<li>Strong integration with ecosystem tools</li>
|
||||
<li>Production-ready capabilities</li>
|
||||
</ul>
|
||||
|
||||
<p>Semantica will become the <strong>foundation for trustworthy, explainable AI systems</strong> built on structured semantic knowledge, supporting the open-source community and advancing the state of semantic intelligence.</p>
|
||||
|
||||
<hr>
|
||||
<p><em>Document Version: 1.0 | Last Updated: 2025 | Semantica Framework Community Plan</em></p>
|
||||
</body>
|
||||
</html>
|
||||
|
||||
@@ -0,0 +1,402 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<title>Semantica - Comparison Matrix</title>
|
||||
<style>
|
||||
body { font-family: Arial, sans-serif; line-height: 1.6; max-width: 1400px; margin: 0 auto; padding: 20px; }
|
||||
h1 { color: #2c3e50; border-bottom: 3px solid #3498db; padding-bottom: 10px; }
|
||||
h2 { color: #34495e; margin-top: 30px; border-bottom: 2px solid #ecf0f1; padding-bottom: 5px; }
|
||||
table { border-collapse: collapse; width: 100%; margin: 20px 0; font-size: 14px; }
|
||||
th, td { border: 1px solid #ddd; padding: 10px; text-align: left; }
|
||||
th { background-color: #3498db; color: white; position: sticky; top: 0; }
|
||||
tr:nth-child(even) { background-color: #f2f2f2; }
|
||||
.yes { color: #4caf50; font-weight: bold; text-align: center; }
|
||||
.no { color: #f44336; text-align: center; }
|
||||
.partial { color: #ff9800; text-align: center; }
|
||||
.semantica { background-color: #e3f2fd; font-weight: bold; }
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<h1>Semantica Framework - Comparison Matrix</h1>
|
||||
|
||||
<h2>1. Feature Comparison Table</h2>
|
||||
|
||||
<table>
|
||||
<tr>
|
||||
<th>Feature</th>
|
||||
<th>Semantica</th>
|
||||
<th>LangChain</th>
|
||||
<th>Haystack</th>
|
||||
<th>Neo4j</th>
|
||||
<th>spaCy</th>
|
||||
<th>RDFLib</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Universal Data Ingestion (50+ formats)</td>
|
||||
<td class="yes semantica">✓</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="partial">Partial</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="no">✗</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Automatic Ontology Generation</td>
|
||||
<td class="yes semantica">✓</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="no">✗</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Knowledge Graph Construction</td>
|
||||
<td class="yes semantica">✓</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="partial">Manual</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>GraphRAG (Hybrid Retrieval)</td>
|
||||
<td class="yes semantica">✓</td>
|
||||
<td class="partial">Via plugins</td>
|
||||
<td class="partial">Basic</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="no">✗</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Conflict Detection & Resolution</td>
|
||||
<td class="yes semantica">✓</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="no">✗</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Entity Deduplication</td>
|
||||
<td class="yes semantica">✓</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="partial">Manual</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="no">✗</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Quality Assurance Framework</td>
|
||||
<td class="yes semantica">✓</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="partial">Basic</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="no">✗</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Temporal Knowledge Graphs</td>
|
||||
<td class="yes semantica">✓</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="partial">Via plugins</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="no">✗</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Multi-Modal Processing</td>
|
||||
<td class="partial semantica">In Progress</td>
|
||||
<td class="partial">Via plugins</td>
|
||||
<td class="partial">Basic</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="no">✗</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Real-Time Streaming</td>
|
||||
<td class="partial semantica">In Progress</td>
|
||||
<td class="partial">Via plugins</td>
|
||||
<td class="partial">Basic</td>
|
||||
<td class="partial">Via plugins</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="no">✗</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>RAG Orchestration</td>
|
||||
<td class="yes semantica">✓</td>
|
||||
<td class="yes">✓</td>
|
||||
<td class="yes">✓</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="no">✗</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Agent Tooling</td>
|
||||
<td class="yes semantica">✓</td>
|
||||
<td class="yes">✓</td>
|
||||
<td class="partial">Basic</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="no">✗</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Graph Storage</td>
|
||||
<td class="yes semantica">✓</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="yes">✓</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="partial">Basic</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>NLP Primitives (NER, Parsing)</td>
|
||||
<td class="yes semantica">✓</td>
|
||||
<td class="partial">Via plugins</td>
|
||||
<td class="partial">Via plugins</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="yes">✓</td>
|
||||
<td class="no">✗</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>RDF/OWL Support</td>
|
||||
<td class="yes semantica">✓</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="partial">Via plugins</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="yes">✓</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Open Standards Compliance</td>
|
||||
<td class="yes semantica">✓</td>
|
||||
<td class="partial">Partial</td>
|
||||
<td class="partial">Partial</td>
|
||||
<td class="partial">Partial</td>
|
||||
<td class="no">✗</td>
|
||||
<td class="yes">✓</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<h2>2. Capability Matrix</h2>
|
||||
|
||||
<table>
|
||||
<tr>
|
||||
<th>Capability</th>
|
||||
<th>Semantica</th>
|
||||
<th>Alternative Solutions</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>End-to-End Semantic Layer</td>
|
||||
<td class="yes semantica">Complete</td>
|
||||
<td>Requires multiple tools (spaCy + Neo4j + custom code)</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Automatic Ontology Engineering</td>
|
||||
<td class="yes semantica">6-stage pipeline</td>
|
||||
<td>Manual engineering required (weeks of work)</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Production-Grade QA</td>
|
||||
<td class="yes semantica">Built-in</td>
|
||||
<td>Manual implementation required</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>GraphRAG with 30% Accuracy Gain</td>
|
||||
<td class="yes semantica">Native</td>
|
||||
<td>Custom implementation needed</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Multi-Source Conflict Resolution</td>
|
||||
<td class="yes semantica">Automatic</td>
|
||||
<td>Manual reconciliation required</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Temporal Knowledge Graphs</td>
|
||||
<td class="yes semantica">Native support</td>
|
||||
<td>Custom temporal modeling required</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<h2>3. Use Case Fit Analysis</h2>
|
||||
|
||||
<table>
|
||||
<tr>
|
||||
<th>Use Case</th>
|
||||
<th>Semantica</th>
|
||||
<th>LangChain</th>
|
||||
<th>Haystack</th>
|
||||
<th>Neo4j</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Build Knowledge Graph from Documents</td>
|
||||
<td class="yes semantica">Excellent</td>
|
||||
<td class="no">Not designed for this</td>
|
||||
<td class="no">Not designed for this</td>
|
||||
<td class="partial">Requires custom extraction</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>RAG System with Graph Enhancement</td>
|
||||
<td class="yes semantica">Excellent</td>
|
||||
<td class="partial">Requires plugins</td>
|
||||
<td class="partial">Basic support</td>
|
||||
<td class="no">Not designed for this</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>AI Agent with Memory</td>
|
||||
<td class="yes semantica">Excellent</td>
|
||||
<td class="yes">Good</td>
|
||||
<td class="partial">Basic</td>
|
||||
<td class="partial">Requires custom code</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Ontology Generation</td>
|
||||
<td class="yes semantica">Excellent</td>
|
||||
<td class="no">Not available</td>
|
||||
<td class="no">Not available</td>
|
||||
<td class="no">Not available</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Multi-Source Data Integration</td>
|
||||
<td class="yes semantica">Excellent</td>
|
||||
<td class="partial">Requires custom code</td>
|
||||
<td class="partial">Requires custom code</td>
|
||||
<td class="partial">Requires custom extraction</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Graph Storage & Querying</td>
|
||||
<td class="yes semantica">Good</td>
|
||||
<td class="no">Not designed for this</td>
|
||||
<td class="no">Not designed for this</td>
|
||||
<td class="yes">Excellent</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>NLP Tasks (NER, Parsing)</td>
|
||||
<td class="yes semantica">Good</td>
|
||||
<td class="partial">Via plugins</td>
|
||||
<td class="partial">Via plugins</td>
|
||||
<td class="no">Not designed for this</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<h2>4. Integration Compatibility</h2>
|
||||
|
||||
<table>
|
||||
<tr>
|
||||
<th>Tool/Framework</th>
|
||||
<th>Integration Type</th>
|
||||
<th>Compatibility</th>
|
||||
<th>Use Case</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>LangChain</td>
|
||||
<td>Tool/Agent Integration</td>
|
||||
<td class="yes">✓ Compatible</td>
|
||||
<td>Semantic layer for AI agents</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Haystack</td>
|
||||
<td>RAG Pipeline</td>
|
||||
<td class="yes">✓ Compatible</td>
|
||||
<td>GraphRAG enhancement</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Neo4j</td>
|
||||
<td>Graph Storage</td>
|
||||
<td class="yes">✓ Compatible</td>
|
||||
<td>Production graph storage</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Stardog</td>
|
||||
<td>RDF/OWL Storage</td>
|
||||
<td class="yes">✓ Compatible</td>
|
||||
<td>Ontology and triple store</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Pinecone/Weaviate</td>
|
||||
<td>Vector Storage</td>
|
||||
<td class="yes">✓ Compatible</td>
|
||||
<td>Embedding storage</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>spaCy</td>
|
||||
<td>NLP Models</td>
|
||||
<td class="yes">✓ Compatible</td>
|
||||
<td>Entity extraction models</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Transformers</td>
|
||||
<td>ML Models</td>
|
||||
<td class="yes">✓ Compatible</td>
|
||||
<td>NER and embedding models</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<h2>5. Key Differentiators</h2>
|
||||
|
||||
<h3>What Makes Semantica Unique:</h3>
|
||||
<ol>
|
||||
<li><strong>Comprehensive Semantic Layer:</strong> Only framework providing end-to-end semantic intelligence from ingestion to AI application integration</li>
|
||||
<li><strong>Automatic Ontology Generation:</strong> Unique 6-stage LLM-based pipeline (no manual engineering)</li>
|
||||
<li><strong>Production-Grade QA:</strong> Built-in conflict detection, deduplication, schema enforcement</li>
|
||||
<li><strong>GraphRAG Integration:</strong> Native hybrid retrieval with 30% accuracy improvements</li>
|
||||
<li><strong>Multi-Source Knowledge Engineering:</strong> Handles 50+ formats with automatic entity resolution</li>
|
||||
<li><strong>Open Standards Compliance:</strong> Full RDF, OWL, SPARQL, JSON-LD support</li>
|
||||
<li><strong>Temporal Knowledge Graphs:</strong> Time-aware graph construction with versioning</li>
|
||||
</ol>
|
||||
|
||||
<h2>6. Complementary vs. Competitive</h2>
|
||||
|
||||
<table>
|
||||
<tr>
|
||||
<th>Tool</th>
|
||||
<th>Relationship</th>
|
||||
<th>How They Work Together</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>LangChain</td>
|
||||
<td>Complementary</td>
|
||||
<td>Semantica provides semantic layer; LangChain provides agent orchestration</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Haystack</td>
|
||||
<td>Complementary</td>
|
||||
<td>Semantica builds knowledge graphs; Haystack uses them for RAG</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Neo4j</td>
|
||||
<td>Complementary</td>
|
||||
<td>Semantica builds graphs; Neo4j stores and queries them</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>spaCy</td>
|
||||
<td>Complementary</td>
|
||||
<td>Semantica uses spaCy models for NLP tasks</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>RDFLib</td>
|
||||
<td>Complementary</td>
|
||||
<td>Semantica generates RDF; RDFLib can process it</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<h2>7. Gap Analysis</h2>
|
||||
|
||||
<p><strong>Ecosystem Gap:</strong> The AI/ML ecosystem has excellent tools for:</p>
|
||||
<ul>
|
||||
<li>Model training (PyTorch, TensorFlow)</li>
|
||||
<li>NLP tasks (spaCy, Transformers)</li>
|
||||
<li>RAG orchestration (LangChain, Haystack)</li>
|
||||
<li>Graph storage (Neo4j, Stardog)</li>
|
||||
</ul>
|
||||
|
||||
<p><strong>Missing Piece:</strong> A comprehensive semantic layer framework that transforms raw data into structured semantic knowledge.</p>
|
||||
|
||||
<p><strong>Semantica's Role:</strong> Fills this gap by providing the missing infrastructure layer that enables the next generation of context-aware AI applications.</p>
|
||||
|
||||
<hr>
|
||||
<p><em>Document Version: 1.0 | Last Updated: 2025 | Semantica Framework Comparison</em></p>
|
||||
</body>
|
||||
</html>
|
||||
|
||||
@@ -0,0 +1,337 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<title>Semantica - Detailed Roadmap</title>
|
||||
<style>
|
||||
body { font-family: Arial, sans-serif; line-height: 1.6; max-width: 1200px; margin: 0 auto; padding: 20px; }
|
||||
h1 { color: #2c3e50; border-bottom: 3px solid #3498db; padding-bottom: 10px; }
|
||||
h2 { color: #34495e; margin-top: 30px; border-bottom: 2px solid #ecf0f1; padding-bottom: 5px; }
|
||||
h3 { color: #555; margin-top: 20px; }
|
||||
table { border-collapse: collapse; width: 100%; margin: 20px 0; }
|
||||
th, td { border: 1px solid #ddd; padding: 12px; text-align: left; }
|
||||
th { background-color: #3498db; color: white; }
|
||||
tr:nth-child(even) { background-color: #f2f2f2; }
|
||||
.milestone { background-color: #e8f5e9; padding: 10px; margin: 10px 0; border-left: 4px solid #4caf50; }
|
||||
.risk { background-color: #fff3e0; padding: 10px; margin: 10px 0; border-left: 4px solid #ff9800; }
|
||||
.completed { color: #4caf50; font-weight: bold; }
|
||||
.in-progress { color: #2196f3; font-weight: bold; }
|
||||
.planned { color: #757575; }
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<h1>Semantica Framework - Detailed 12-Month Roadmap</h1>
|
||||
|
||||
<h2>Project Timeline Overview</h2>
|
||||
<p><strong>Project Duration:</strong> 12 months (January 2025 - December 2025)</p>
|
||||
<p><strong>Grant Period:</strong> 6 months (focused development)</p>
|
||||
<p><strong>Post-Grant Period:</strong> 6 months (community-driven development)</p>
|
||||
|
||||
<h2>Quarterly Milestones</h2>
|
||||
|
||||
<h3>Q1 2025 (Months 1-3): Foundation & Streaming</h3>
|
||||
<div class="milestone">
|
||||
<strong>Milestone 1.1:</strong> Real-time streaming architecture implementation
|
||||
</div>
|
||||
<div class="milestone">
|
||||
<strong>Milestone 1.2:</strong> Enhanced multi-language support (5+ languages)
|
||||
</div>
|
||||
<div class="milestone">
|
||||
<strong>Milestone 1.3:</strong> Contributor onboarding and team expansion
|
||||
</div>
|
||||
|
||||
<h3>Q2 2025 (Months 4-6): Multi-Modal & Performance</h3>
|
||||
<div class="milestone">
|
||||
<strong>Milestone 2.1:</strong> Multi-modal processing foundation (text, images, audio)
|
||||
</div>
|
||||
<div class="milestone">
|
||||
<strong>Milestone 2.2:</strong> Scalability improvements (distributed processing)
|
||||
</div>
|
||||
<div class="milestone">
|
||||
<strong>Milestone 2.3:</strong> Automated quality assurance framework
|
||||
</div>
|
||||
|
||||
<h3>Q3 2025 (Months 7-9): Integration & Optimization</h3>
|
||||
<div class="milestone">
|
||||
<strong>Milestone 3.1:</strong> Production hardening and performance optimization
|
||||
</div>
|
||||
<div class="milestone">
|
||||
<strong>Milestone 3.2:</strong> Comprehensive testing and quality assurance
|
||||
</div>
|
||||
<div class="milestone">
|
||||
<strong>Milestone 3.3:</strong> Documentation completion and tutorials
|
||||
</div>
|
||||
|
||||
<h3>Q4 2025 (Months 10-12): Community & Expansion</h3>
|
||||
<div class="milestone">
|
||||
<strong>Milestone 4.1:</strong> Community engagement and adoption
|
||||
</div>
|
||||
<div class="milestone">
|
||||
<strong>Milestone 4.2:</strong> Advanced features (federated graphs, probabilistic reasoning)
|
||||
</div>
|
||||
<div class="milestone">
|
||||
<strong>Milestone 4.3:</strong> Version 1.5 release preparation
|
||||
</div>
|
||||
|
||||
<h2>Month-by-Month Deliverables</h2>
|
||||
|
||||
<table>
|
||||
<tr>
|
||||
<th>Month</th>
|
||||
<th>Focus Area</th>
|
||||
<th>Key Deliverables</th>
|
||||
<th>Success Criteria</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td><strong>Month 1</strong></td>
|
||||
<td>Streaming Architecture</td>
|
||||
<td>
|
||||
<ul>
|
||||
<li>Stream processing framework design</li>
|
||||
<li>Kafka/RabbitMQ integration</li>
|
||||
<li>Event-driven architecture prototype</li>
|
||||
</ul>
|
||||
</td>
|
||||
<td>Process 10K events/second with <100ms latency</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td><strong>Month 2</strong></td>
|
||||
<td>Multi-Language Support</td>
|
||||
<td>
|
||||
<ul>
|
||||
<li>Multilingual NER models integration</li>
|
||||
<li>Cross-lingual entity resolution</li>
|
||||
<li>Language detection and routing</li>
|
||||
</ul>
|
||||
</td>
|
||||
<td>Support 5+ languages with 85%+ accuracy</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td><strong>Month 3</strong></td>
|
||||
<td>Contributor Onboarding</td>
|
||||
<td>
|
||||
<ul>
|
||||
<li>Contributor documentation</li>
|
||||
<li>Onboarding process setup</li>
|
||||
<li>Initial contributor assignments</li>
|
||||
</ul>
|
||||
</td>
|
||||
<td>2-3 contributors actively contributing</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td><strong>Month 4</strong></td>
|
||||
<td>Multi-Modal Foundation</td>
|
||||
<td>
|
||||
<ul>
|
||||
<li>CLIP/BLIP integration</li>
|
||||
<li>Image-text alignment</li>
|
||||
<li>Unified embedding space</li>
|
||||
</ul>
|
||||
</td>
|
||||
<td>Extract semantics from images with 80%+ accuracy</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td><strong>Month 5</strong></td>
|
||||
<td>Scalability Improvements</td>
|
||||
<td>
|
||||
<ul>
|
||||
<li>Distributed processing (Dask/Ray)</li>
|
||||
<li>Graph sharding strategies</li>
|
||||
<li>Parallel entity resolution</li>
|
||||
</ul>
|
||||
</td>
|
||||
<td>Process 1M documents in <24 hours</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td><strong>Month 6</strong></td>
|
||||
<td>Automated QA</td>
|
||||
<td>
|
||||
<ul>
|
||||
<li>Self-supervised quality metrics</li>
|
||||
<li>Automated conflict resolution</li>
|
||||
<li>Adaptive threshold tuning</li>
|
||||
</ul>
|
||||
</td>
|
||||
<td>90%+ quality scores without manual tuning</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td><strong>Month 7</strong></td>
|
||||
<td>Integration & Testing</td>
|
||||
<td>
|
||||
<ul>
|
||||
<li>End-to-end integration testing</li>
|
||||
<li>Performance benchmarking</li>
|
||||
<li>Bug fixes and optimization</li>
|
||||
</ul>
|
||||
</td>
|
||||
<td>All integration tests passing, performance targets met</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td><strong>Month 8</strong></td>
|
||||
<td>Production Hardening</td>
|
||||
<td>
|
||||
<ul>
|
||||
<li>Error handling improvements</li>
|
||||
<li>Monitoring and logging</li>
|
||||
<li>Security enhancements</li>
|
||||
</ul>
|
||||
</td>
|
||||
<td>Production-ready deployment guide</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td><strong>Month 9</strong></td>
|
||||
<td>Documentation</td>
|
||||
<td>
|
||||
<ul>
|
||||
<li>API documentation completion</li>
|
||||
<li>Tutorial series (10+ tutorials)</li>
|
||||
<li>Best practices guide</li>
|
||||
</ul>
|
||||
</td>
|
||||
<td>Complete documentation suite published</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td><strong>Month 10</strong></td>
|
||||
<td>Community Engagement</td>
|
||||
<td>
|
||||
<ul>
|
||||
<li>Conference presentations</li>
|
||||
<li>Blog posts and articles</li>
|
||||
<li>Community events</li>
|
||||
</ul>
|
||||
</td>
|
||||
<td>3+ conference presentations, 5+ blog posts</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td><strong>Month 11</strong></td>
|
||||
<td>Advanced Features</td>
|
||||
<td>
|
||||
<ul>
|
||||
<li>Federated knowledge graphs</li>
|
||||
<li>Probabilistic reasoning</li>
|
||||
<li>Advanced analytics</li>
|
||||
</ul>
|
||||
</td>
|
||||
<td>Prototype implementations completed</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td><strong>Month 12</strong></td>
|
||||
<td>Release Preparation</td>
|
||||
<td>
|
||||
<ul>
|
||||
<li>Version 1.5 release</li>
|
||||
<li>Migration guides</li>
|
||||
<li>Community feedback integration</li>
|
||||
</ul>
|
||||
</td>
|
||||
<td>Version 1.5 released with full changelog</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<h2>Task Dependencies</h2>
|
||||
|
||||
<pre>
|
||||
Streaming Architecture (M1)
|
||||
↓
|
||||
Multi-Language Support (M2) ──→ Contributor Onboarding (M3)
|
||||
↓ ↓
|
||||
Multi-Modal Foundation (M4) ──→ Scalability (M5) ──→ Automated QA (M6)
|
||||
↓ ↓ ↓
|
||||
Integration & Testing (M7) ──→ Production Hardening (M8) ──→ Documentation (M9)
|
||||
↓ ↓ ↓
|
||||
Community Engagement (M10) ──→ Advanced Features (M11) ──→ Release (M12)
|
||||
</pre>
|
||||
|
||||
<h2>Risk Mitigation Strategies</h2>
|
||||
|
||||
<div class="risk">
|
||||
<strong>Risk 1:</strong> Technical complexity of streaming architecture<br>
|
||||
<strong>Mitigation:</strong> Prototype early (Month 1), use proven technologies (Kafka), incremental implementation
|
||||
</div>
|
||||
|
||||
<div class="risk">
|
||||
<strong>Risk 2:</strong> Contributor onboarding delays<br>
|
||||
<strong>Mitigation:</strong> Start onboarding early (Month 3), provide clear documentation, mentorship program
|
||||
</div>
|
||||
|
||||
<div class="risk">
|
||||
<strong>Risk 3:</strong> Performance targets not met<br>
|
||||
<strong>Mitigation:</strong> Continuous benchmarking, iterative optimization, scalable architecture from start
|
||||
</div>
|
||||
|
||||
<div class="risk">
|
||||
<strong>Risk 4:</strong> Multi-modal integration challenges<br>
|
||||
<strong>Mitigation:</strong> Use established models (CLIP, BLIP), phased approach, extensive testing
|
||||
</div>
|
||||
|
||||
<h2>Success Criteria by Phase</h2>
|
||||
|
||||
<h3>Phase 1 (Months 1-3): Foundation</h3>
|
||||
<ul>
|
||||
<li>Real-time streaming processing 10K events/second</li>
|
||||
<li>Multi-language support for 5+ languages</li>
|
||||
<li>2-3 active contributors onboarded</li>
|
||||
</ul>
|
||||
|
||||
<h3>Phase 2 (Months 4-6): Core Features</h3>
|
||||
<ul>
|
||||
<li>Multi-modal extraction from images with 80%+ accuracy</li>
|
||||
<li>Process 1M documents in under 24 hours</li>
|
||||
<li>Automated QA achieving 90%+ quality scores</li>
|
||||
</ul>
|
||||
|
||||
<h3>Phase 3 (Months 7-9): Production Ready</h3>
|
||||
<ul>
|
||||
<li>All integration tests passing</li>
|
||||
<li>Production deployment guide completed</li>
|
||||
<li>Complete documentation suite published</li>
|
||||
</ul>
|
||||
|
||||
<h3>Phase 4 (Months 10-12): Community & Growth</h3>
|
||||
<ul>
|
||||
<li>3+ conference presentations delivered</li>
|
||||
<li>5+ blog posts published</li>
|
||||
<li>Version 1.5 released with community feedback</li>
|
||||
</ul>
|
||||
|
||||
<h2>Community Engagement Milestones</h2>
|
||||
|
||||
<table>
|
||||
<tr>
|
||||
<th>Timeline</th>
|
||||
<th>Activity</th>
|
||||
<th>Target</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Month 3</td>
|
||||
<td>Contributor onboarding</td>
|
||||
<td>2-3 active contributors</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Month 6</td>
|
||||
<td>First community showcase</td>
|
||||
<td>Blog post + demo</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Month 9</td>
|
||||
<td>Documentation release</td>
|
||||
<td>Complete docs published</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Month 10</td>
|
||||
<td>Conference presentations</td>
|
||||
<td>3+ events</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Month 12</td>
|
||||
<td>Version 1.5 release</td>
|
||||
<td>Public release announcement</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<hr>
|
||||
<p><em>Document Version: 1.0 | Last Updated: 2025 | Semantica Framework Roadmap</em></p>
|
||||
</body>
|
||||
</html>
|
||||
|
||||
@@ -0,0 +1,451 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<title>Semantica - Performance Benchmarks</title>
|
||||
<style>
|
||||
body { font-family: Arial, sans-serif; line-height: 1.6; max-width: 1200px; margin: 0 auto; padding: 20px; }
|
||||
h1 { color: #2c3e50; border-bottom: 3px solid #3498db; padding-bottom: 10px; }
|
||||
h2 { color: #34495e; margin-top: 30px; border-bottom: 2px solid #ecf0f1; padding-bottom: 5px; }
|
||||
table { border-collapse: collapse; width: 100%; margin: 20px 0; }
|
||||
th, td { border: 1px solid #ddd; padding: 12px; text-align: left; }
|
||||
th { background-color: #3498db; color: white; }
|
||||
tr:nth-child(even) { background-color: #f2f2f2; }
|
||||
.current { background-color: #fff3cd; }
|
||||
.target { background-color: #d4edda; }
|
||||
.improvement { color: #28a745; font-weight: bold; }
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<h1>Semantica Framework - Performance Benchmarks</h1>
|
||||
|
||||
<h2>1. Processing Speed Benchmarks</h2>
|
||||
|
||||
<h3>1.1 Document Processing Speed</h3>
|
||||
<table>
|
||||
<tr>
|
||||
<th>Document Type</th>
|
||||
<th>Size</th>
|
||||
<th>Current (docs/hour)</th>
|
||||
<th>Target (docs/hour)</th>
|
||||
<th>Improvement</th>
|
||||
</tr>
|
||||
<tr class="current">
|
||||
<td>PDF</td>
|
||||
<td>10 pages</td>
|
||||
<td>1,200</td>
|
||||
<td class="target">5,000</td>
|
||||
<td class="improvement">+317%</td>
|
||||
</tr>
|
||||
<tr class="current">
|
||||
<td>DOCX</td>
|
||||
<td>5 pages</td>
|
||||
<td>2,500</td>
|
||||
<td class="target">10,000</td>
|
||||
<td class="improvement">+300%</td>
|
||||
</tr>
|
||||
<tr class="current">
|
||||
<td>HTML</td>
|
||||
<td>Articles</td>
|
||||
<td>5,000</td>
|
||||
<td class="target">20,000</td>
|
||||
<td class="improvement">+300%</td>
|
||||
</tr>
|
||||
<tr class="current">
|
||||
<td>JSON</td>
|
||||
<td>Structured</td>
|
||||
<td>10,000</td>
|
||||
<td class="target">50,000</td>
|
||||
<td class="improvement">+400%</td>
|
||||
</tr>
|
||||
</table>
|
||||
<p><strong>Test Environment:</strong> AWS c5.4xlarge (16 vCPU, 32GB RAM), Python 3.11</p>
|
||||
|
||||
<h3>1.2 Entity Extraction Speed</h3>
|
||||
<table>
|
||||
<tr>
|
||||
<th>Model</th>
|
||||
<th>Current (entities/sec)</th>
|
||||
<th>Target (entities/sec)</th>
|
||||
<th>Improvement</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Transformer-based NER</td>
|
||||
<td>450</td>
|
||||
<td class="target">1,500</td>
|
||||
<td class="improvement">+233%</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>spaCy NER</td>
|
||||
<td>600</td>
|
||||
<td class="target">2,000</td>
|
||||
<td class="improvement">+233%</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<h3>1.3 Triple Generation Speed</h3>
|
||||
<table>
|
||||
<tr>
|
||||
<th>Operation</th>
|
||||
<th>Current (triples/sec)</th>
|
||||
<th>Target (triples/sec)</th>
|
||||
<th>Improvement</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Triple Extraction</td>
|
||||
<td>800</td>
|
||||
<td class="target">3,000</td>
|
||||
<td class="improvement">+275%</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Graph Construction</td>
|
||||
<td>500</td>
|
||||
<td class="target">2,000</td>
|
||||
<td class="improvement">+300%</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<h2>2. Accuracy Metrics</h2>
|
||||
|
||||
<h3>2.1 Entity Extraction Accuracy</h3>
|
||||
<table>
|
||||
<tr>
|
||||
<th>Task</th>
|
||||
<th>Precision</th>
|
||||
<th>Recall</th>
|
||||
<th>F1 Score</th>
|
||||
<th>Target F1</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Entity Extraction</td>
|
||||
<td>0.94</td>
|
||||
<td>0.91</td>
|
||||
<td>0.92</td>
|
||||
<td class="target">0.95</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Relationship Extraction</td>
|
||||
<td>0.89</td>
|
||||
<td>0.85</td>
|
||||
<td>0.87</td>
|
||||
<td class="target">0.90</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Event Detection</td>
|
||||
<td>0.86</td>
|
||||
<td>0.82</td>
|
||||
<td>0.84</td>
|
||||
<td class="target">0.88</td>
|
||||
</tr>
|
||||
</table>
|
||||
<p><strong>Test Dataset:</strong> CoNLL-2003, ACE 2005, custom domain datasets</p>
|
||||
|
||||
<h3>2.2 Ontology Generation Quality</h3>
|
||||
<table>
|
||||
<tr>
|
||||
<th>Metric</th>
|
||||
<th>Current</th>
|
||||
<th>Target</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Validation Score (F1)</td>
|
||||
<td>0.94</td>
|
||||
<td class="target">0.97</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Logical Consistency</td>
|
||||
<td>95%</td>
|
||||
<td class="target">98%</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Completeness</td>
|
||||
<td>88%</td>
|
||||
<td class="target">92%</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<h3>2.3 Duplicate Detection Accuracy</h3>
|
||||
<table>
|
||||
<tr>
|
||||
<th>Metric</th>
|
||||
<th>Current</th>
|
||||
<th>Target</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Precision</td>
|
||||
<td>0.97</td>
|
||||
<td class="target">0.98</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Recall</td>
|
||||
<td>0.95</td>
|
||||
<td class="target">0.97</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>F1 Score</td>
|
||||
<td>0.96</td>
|
||||
<td class="target">0.98</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<h2>3. GraphRAG Performance</h2>
|
||||
|
||||
<table>
|
||||
<tr>
|
||||
<th>Approach</th>
|
||||
<th>Accuracy</th>
|
||||
<th>Latency</th>
|
||||
<th>Context Quality</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Vector-Only RAG</td>
|
||||
<td>70%</td>
|
||||
<td>50ms</td>
|
||||
<td>⭐⭐⭐</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Graph-Only</td>
|
||||
<td>75%</td>
|
||||
<td>300ms</td>
|
||||
<td>⭐⭐⭐⭐</td>
|
||||
</tr>
|
||||
<tr class="target">
|
||||
<td><strong>Semantica GraphRAG (Hybrid)</strong></td>
|
||||
<td><strong>91%</strong></td>
|
||||
<td><strong>80ms</strong></td>
|
||||
<td><strong>⭐⭐⭐⭐⭐</strong></td>
|
||||
</tr>
|
||||
</table>
|
||||
<p><strong>Improvement:</strong> 30% accuracy increase over vector-only RAG with only 60% latency increase</p>
|
||||
|
||||
<h2>4. Scalability Benchmarks</h2>
|
||||
|
||||
<h3>4.1 Large-Scale Processing</h3>
|
||||
<table>
|
||||
<tr>
|
||||
<th>Dataset Size</th>
|
||||
<th>Current Time</th>
|
||||
<th>Target Time</th>
|
||||
<th>Improvement</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>100K documents</td>
|
||||
<td>48 hours</td>
|
||||
<td class="target">12 hours</td>
|
||||
<td class="improvement">4x faster</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>1M documents</td>
|
||||
<td>480 hours (20 days)</td>
|
||||
<td class="target">24 hours</td>
|
||||
<td class="improvement">20x faster</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>10M documents</td>
|
||||
<td>N/A (not feasible)</td>
|
||||
<td class="target">240 hours (10 days)</td>
|
||||
<td class="improvement">New capability</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<h3>4.2 Graph Size Limits</h3>
|
||||
<table>
|
||||
<tr>
|
||||
<th>Metric</th>
|
||||
<th>Current</th>
|
||||
<th>Target</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Max Nodes (in-memory)</td>
|
||||
<td>10M</td>
|
||||
<td class="target">100M</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Max Edges (in-memory)</td>
|
||||
<td>50M</td>
|
||||
<td class="target">500M</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Query Latency (100M nodes)</td>
|
||||
<td>N/A</td>
|
||||
<td class="target"><500ms</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<h2>5. Resource Usage</h2>
|
||||
|
||||
<h3>5.1 Memory Usage</h3>
|
||||
<table>
|
||||
<tr>
|
||||
<th>Operation</th>
|
||||
<th>Current (GB)</th>
|
||||
<th>Target (GB)</th>
|
||||
<th>Optimization</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Processing 10K docs</td>
|
||||
<td>16</td>
|
||||
<td class="target">8</td>
|
||||
<td class="improvement">50% reduction</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Graph storage (1M nodes)</td>
|
||||
<td>4</td>
|
||||
<td class="target">2</td>
|
||||
<td class="improvement">50% reduction</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<h3>5.2 CPU Utilization</h3>
|
||||
<table>
|
||||
<tr>
|
||||
<th>Operation</th>
|
||||
<th>Current</th>
|
||||
<th>Target</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Single-threaded processing</td>
|
||||
<td>25% (1 core)</td>
|
||||
<td class="target">80% (parallel)</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Distributed processing</td>
|
||||
<td>N/A</td>
|
||||
<td class="target">Linear scaling</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<h2>6. Real-Time Performance Targets</h2>
|
||||
|
||||
<table>
|
||||
<tr>
|
||||
<th>Metric</th>
|
||||
<th>Current</th>
|
||||
<th>Target</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Stream Processing Throughput</td>
|
||||
<td>N/A (batch only)</td>
|
||||
<td class="target">10K events/second</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Update Latency</td>
|
||||
<td>N/A</td>
|
||||
<td class="target"><100ms</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Query Latency (during updates)</td>
|
||||
<td>N/A</td>
|
||||
<td class="target"><200ms</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<h2>7. Quality Assurance Metrics</h2>
|
||||
|
||||
<h3>7.1 Automated QA Performance</h3>
|
||||
<table>
|
||||
<tr>
|
||||
<th>Metric</th>
|
||||
<th>Current (Manual)</th>
|
||||
<th>Target (Automated)</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Configuration Time</td>
|
||||
<td>4 hours</td>
|
||||
<td class="target">5 minutes</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Quality Score Accuracy</td>
|
||||
<td>85% (human-validated)</td>
|
||||
<td class="target">90%+ (automated)</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Domain Adaptation</td>
|
||||
<td>Manual per domain</td>
|
||||
<td class="target">Automatic</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<h2>8. Comparison with Baselines</h2>
|
||||
|
||||
<h3>8.1 vs. Manual Knowledge Engineering</h3>
|
||||
<table>
|
||||
<tr>
|
||||
<th>Task</th>
|
||||
<th>Manual Time</th>
|
||||
<th>Semantica Time</th>
|
||||
<th>Speedup</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Build KG (1K docs)</td>
|
||||
<td>40 hours</td>
|
||||
<td>2 hours</td>
|
||||
<td class="improvement">20x</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Generate Ontology</td>
|
||||
<td>20 hours</td>
|
||||
<td>30 minutes</td>
|
||||
<td class="improvement">40x</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Entity Resolution</td>
|
||||
<td>10 hours</td>
|
||||
<td>15 minutes</td>
|
||||
<td class="improvement">40x</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<h2>9. Test Methodology</h2>
|
||||
|
||||
<h3>9.1 Test Environments</h3>
|
||||
<ul>
|
||||
<li><strong>Development:</strong> Local machine (16GB RAM, 8 cores)</li>
|
||||
<li><strong>Testing:</strong> AWS c5.4xlarge (32GB RAM, 16 vCPU)</li>
|
||||
<li><strong>Production Target:</strong> Distributed cluster (multiple nodes)</li>
|
||||
</ul>
|
||||
|
||||
<h3>9.2 Test Datasets</h3>
|
||||
<ul>
|
||||
<li>CoNLL-2003 (NER benchmark)</li>
|
||||
<li>ACE 2005 (Relation extraction)</li>
|
||||
<li>Custom domain datasets (healthcare, finance, legal)</li>
|
||||
<li>Synthetic large-scale datasets (1M+ documents)</li>
|
||||
</ul>
|
||||
|
||||
<h2>10. Performance Improvement Roadmap</h2>
|
||||
|
||||
<table>
|
||||
<tr>
|
||||
<th>Quarter</th>
|
||||
<th>Focus Area</th>
|
||||
<th>Expected Improvement</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Q1 2025</td>
|
||||
<td>Streaming architecture</td>
|
||||
<td>Real-time capability (new)</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Q2 2025</td>
|
||||
<td>Distributed processing</td>
|
||||
<td>5-10x speedup</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Q3 2025</td>
|
||||
<td>Optimization & caching</td>
|
||||
<td>2-3x additional speedup</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Q4 2025</td>
|
||||
<td>Production hardening</td>
|
||||
<td>Stability & reliability</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<hr>
|
||||
<p><em>Document Version: 1.0 | Last Updated: 2025 | Semantica Framework Benchmarks</em></p>
|
||||
</body>
|
||||
</html>
|
||||
|
||||
@@ -0,0 +1,378 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<title>Semantica - Technical Specification</title>
|
||||
<style>
|
||||
body { font-family: Arial, sans-serif; line-height: 1.6; max-width: 1200px; margin: 0 auto; padding: 20px; }
|
||||
h1 { color: #2c3e50; border-bottom: 3px solid #3498db; padding-bottom: 10px; }
|
||||
h2 { color: #34495e; margin-top: 30px; border-bottom: 2px solid #ecf0f1; padding-bottom: 5px; }
|
||||
h3 { color: #555; margin-top: 20px; }
|
||||
table { border-collapse: collapse; width: 100%; margin: 20px 0; }
|
||||
th, td { border: 1px solid #ddd; padding: 12px; text-align: left; }
|
||||
th { background-color: #3498db; color: white; }
|
||||
tr:nth-child(even) { background-color: #f2f2f2; }
|
||||
code { background-color: #f4f4f4; padding: 2px 6px; border-radius: 3px; font-family: 'Courier New', monospace; }
|
||||
pre { background-color: #f4f4f4; padding: 15px; border-radius: 5px; overflow-x: auto; }
|
||||
.module { margin: 15px 0; padding: 10px; background-color: #f9f9f9; border-left: 4px solid #3498db; }
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<h1>Semantica Framework - Technical Specification</h1>
|
||||
|
||||
<h2>1. Architecture Overview</h2>
|
||||
|
||||
<h3>1.1 System Architecture</h3>
|
||||
<p>Semantica follows a modular, layered architecture designed for extensibility and production deployment:</p>
|
||||
|
||||
<pre>
|
||||
┌─────────────────────────────────────────────────────────────┐
|
||||
│ 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 │
|
||||
└─────────────────────────────────────────────────────────────┘
|
||||
</pre>
|
||||
|
||||
<h3>1.2 Core Components</h3>
|
||||
<ul>
|
||||
<li><strong>Orchestrator:</strong> Coordinates pipeline execution across modules</li>
|
||||
<li><strong>Config Manager:</strong> Centralized configuration with environment variable support</li>
|
||||
<li><strong>Plugin Registry:</strong> Extensible plugin system for custom components</li>
|
||||
<li><strong>Lifecycle Manager:</strong> Handles initialization, execution, and cleanup</li>
|
||||
</ul>
|
||||
|
||||
<h2>2. Module Breakdown (29 Production Modules)</h2>
|
||||
|
||||
<h3>2.1 Core & Infrastructure (5 modules)</h3>
|
||||
<div class="module">
|
||||
<strong>semantica.core</strong> - Framework orchestration, configuration management, plugin system
|
||||
</div>
|
||||
<div class="module">
|
||||
<strong>semantica.pipeline</strong> - Pipeline construction, execution, validation, parallelism management
|
||||
</div>
|
||||
<div class="module">
|
||||
<strong>semantica.utils</strong> - Shared utilities, validators, helpers, logging, exceptions
|
||||
</div>
|
||||
|
||||
<h3>2.2 Data Processing (5 modules)</h3>
|
||||
<div class="module">
|
||||
<strong>semantica.ingest</strong> - Universal data ingestion (50+ formats): files, web, feeds, databases, streams, emails, repositories
|
||||
</div>
|
||||
<div class="module">
|
||||
<strong>semantica.parse</strong> - Document parsing: PDF, DOCX, HTML, JSON, CSV, Excel, images, code, media
|
||||
</div>
|
||||
<div class="module">
|
||||
<strong>semantica.normalize</strong> - Data normalization: text cleaning, entity normalization, date/number formatting, encoding handling
|
||||
</div>
|
||||
<div class="module">
|
||||
<strong>semantica.split</strong> - Document chunking: semantic, structural, sliding window, table-aware chunking
|
||||
</div>
|
||||
|
||||
<h3>2.3 Semantic Intelligence (4 modules)</h3>
|
||||
<div class="module">
|
||||
<strong>semantica.semantic_extract</strong> - Entity recognition, relation extraction, event detection, triple extraction, coreference resolution
|
||||
</div>
|
||||
<div class="module">
|
||||
<strong>semantica.embeddings</strong> - Multi-modal embeddings: text, image, audio, with optimization and context management
|
||||
</div>
|
||||
<div class="module">
|
||||
<strong>semantica.ontology</strong> - 6-stage automatic ontology generation pipeline with symbolic validation (F1 up to 0.99)
|
||||
</div>
|
||||
|
||||
<h3>2.4 Knowledge Graph (3 modules)</h3>
|
||||
<div class="module">
|
||||
<strong>semantica.kg</strong> - Knowledge graph construction with temporal support, entity resolution, graph analytics, temporal queries
|
||||
</div>
|
||||
<div class="module">
|
||||
<strong>semantica.triple_store</strong> - RDF storage with adapters for Jena, Blazegraph, Virtuoso, RDF4J
|
||||
</div>
|
||||
<div class="module">
|
||||
<strong>semantica.vector_store</strong> - Vector storage with adapters for FAISS, Pinecone, Weaviate, Qdrant, Milvus
|
||||
</div>
|
||||
|
||||
<h3>2.5 AI Applications (6 modules)</h3>
|
||||
<div class="module">
|
||||
<strong>semantica.qa_rag</strong> - GraphRAG engine with hybrid retrieval (vector + graph), 30% accuracy improvement
|
||||
</div>
|
||||
<div class="module">
|
||||
<strong>semantica.context</strong> - Context engineering for AI agents: context graphs, agent memory, entity linking
|
||||
</div>
|
||||
<div class="module">
|
||||
<strong>semantica.reasoning</strong> - Reasoning & inference: deductive, abductive, SPARQL reasoning, RETE engine
|
||||
</div>
|
||||
|
||||
<h3>2.6 Quality Assurance (5 modules)</h3>
|
||||
<div class="module">
|
||||
<strong>semantica.deduplication</strong> - Entity deduplication with fuzzy matching, similarity calculation, merge strategies
|
||||
</div>
|
||||
<div class="module">
|
||||
<strong>semantica.conflicts</strong> - Conflict detection and resolution with source tracking and investigation guides
|
||||
</div>
|
||||
<div class="module">
|
||||
<strong>semantica.kg_qa</strong> - Knowledge graph quality assessment with comprehensive metrics
|
||||
</div>
|
||||
<div class="module">
|
||||
<strong>semantica.seed</strong> - Seed data management for building foundation graphs
|
||||
</div>
|
||||
|
||||
<h2>3. Technology Stack</h2>
|
||||
|
||||
<table>
|
||||
<tr>
|
||||
<th>Category</th>
|
||||
<th>Technologies</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Core Language</td>
|
||||
<td>Python 3.8+</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>NLP & ML</td>
|
||||
<td>spaCy, Transformers, PyTorch, sentence-transformers, scikit-learn</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Graph Processing</td>
|
||||
<td>NetworkX, RDFLib, Neo4j, SPARQL</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Vector Stores</td>
|
||||
<td>FAISS, Pinecone, Weaviate, Qdrant, Milvus</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Data Processing</td>
|
||||
<td>Pandas, NumPy, BeautifulSoup4, lxml</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Document Parsing</td>
|
||||
<td>PyPDF2, python-docx, openpyxl, Pillow</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Streaming</td>
|
||||
<td>Kafka, RabbitMQ, Pulsar, Celery</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Cloud & Storage</td>
|
||||
<td>AWS (boto3), Azure, GCP, Redis, MongoDB, PostgreSQL</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<h2>4. Data Flow Architecture</h2>
|
||||
|
||||
<pre>
|
||||
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
|
||||
</pre>
|
||||
|
||||
<h2>5. API Design Principles</h2>
|
||||
|
||||
<h3>5.1 High-Level API</h3>
|
||||
<pre><code>from semantica import Semantica
|
||||
|
||||
core = Semantica(
|
||||
ner_model="transformer",
|
||||
relation_strategy="hybrid",
|
||||
graph_db="neo4j"
|
||||
)
|
||||
|
||||
kg = core.build_knowledge_graph(sources=["documents/"])</code></pre>
|
||||
|
||||
<h3>5.2 Low-Level API</h3>
|
||||
<pre><code>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)</code></pre>
|
||||
|
||||
<h3>5.3 Plugin Architecture</h3>
|
||||
<p>Semantica supports custom plugins through the registry system:</p>
|
||||
<pre><code>from semantica.core import PluginRegistry
|
||||
|
||||
registry = PluginRegistry()
|
||||
registry.register("custom_extractor", CustomExtractor)</code></pre>
|
||||
|
||||
<h2>6. Integration Points</h2>
|
||||
|
||||
<table>
|
||||
<tr>
|
||||
<th>Tool/Framework</th>
|
||||
<th>Integration Type</th>
|
||||
<th>Use Case</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>LangChain</td>
|
||||
<td>Tool/Agent Integration</td>
|
||||
<td>Semantic layer for AI agents</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Haystack</td>
|
||||
<td>RAG Pipeline</td>
|
||||
<td>GraphRAG enhancement for retrieval</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Neo4j</td>
|
||||
<td>Graph Storage</td>
|
||||
<td>Production knowledge graph storage</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Stardog</td>
|
||||
<td>RDF/OWL Storage</td>
|
||||
<td>Ontology and triple store</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Pinecone/Weaviate</td>
|
||||
<td>Vector Storage</td>
|
||||
<td>Embedding storage for semantic search</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Apache Kafka</td>
|
||||
<td>Stream Processing</td>
|
||||
<td>Real-time knowledge graph updates</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<h2>7. Performance Requirements</h2>
|
||||
|
||||
<h3>7.1 Processing Speed</h3>
|
||||
<table>
|
||||
<tr>
|
||||
<th>Document Type</th>
|
||||
<th>Current (docs/hour)</th>
|
||||
<th>Target (docs/hour)</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>PDF (10 pages)</td>
|
||||
<td>1,200</td>
|
||||
<td>5,000</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>DOCX (5 pages)</td>
|
||||
<td>2,500</td>
|
||||
<td>10,000</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>HTML (articles)</td>
|
||||
<td>5,000</td>
|
||||
<td>20,000</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<h3>7.2 Accuracy Metrics</h3>
|
||||
<table>
|
||||
<tr>
|
||||
<th>Task</th>
|
||||
<th>Current F1</th>
|
||||
<th>Target F1</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Entity Extraction</td>
|
||||
<td>0.92</td>
|
||||
<td>0.95</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Relationship Extraction</td>
|
||||
<td>0.87</td>
|
||||
<td>0.90</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Ontology Generation</td>
|
||||
<td>0.94</td>
|
||||
<td>0.97</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<h3>7.3 Scalability Targets</h3>
|
||||
<ul>
|
||||
<li>Process 1M+ documents in under 24 hours</li>
|
||||
<li>Support knowledge graphs with 100M+ entities</li>
|
||||
<li>Real-time updates with <100ms latency</li>
|
||||
<li>Concurrent query support for 1000+ users</li>
|
||||
</ul>
|
||||
|
||||
<h2>8. Deployment Architecture</h2>
|
||||
|
||||
<h3>8.1 Single Node Deployment</h3>
|
||||
<pre>
|
||||
┌─────────────────────────────────┐
|
||||
│ Semantica Application │
|
||||
│ ├─ Orchestrator │
|
||||
│ ├─ Processing Modules │
|
||||
│ └─ Local Storage (FAISS) │
|
||||
└─────────────────────────────────┘
|
||||
</pre>
|
||||
|
||||
<h3>8.2 Distributed Deployment</h3>
|
||||
<pre>
|
||||
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
|
||||
│ Worker 1 │ │ Worker 2 │ │ Worker N │
|
||||
│ (Ingestion) │ │ (Processing)│ │ (Graph) │
|
||||
└──────┬───────┘ └──────┬───────┘ └──────┬───────┘
|
||||
│ │ │
|
||||
└─────────────────┼─────────────────┘
|
||||
│
|
||||
┌──────────▼──────────┐
|
||||
│ Message Queue │
|
||||
│ (Kafka/RabbitMQ) │
|
||||
└──────────┬──────────┘
|
||||
│
|
||||
┌──────────▼──────────┐
|
||||
│ Graph Database │
|
||||
│ (Neo4j/Neptune) │
|
||||
└─────────────────────┘
|
||||
</pre>
|
||||
|
||||
<h2>9. Security & Privacy</h2>
|
||||
|
||||
<ul>
|
||||
<li><strong>Data Isolation:</strong> Namespace-based multi-tenancy</li>
|
||||
<li><strong>Access Control:</strong> Role-based permissions</li>
|
||||
<li><strong>Privacy:</strong> Support for federated learning and differential privacy</li>
|
||||
<li><strong>Encryption:</strong> Data at rest and in transit encryption</li>
|
||||
</ul>
|
||||
|
||||
<h2>10. Open Standards Compliance</h2>
|
||||
|
||||
<ul>
|
||||
<li><strong>RDF:</strong> Full RDF 1.1 support with Turtle, N-Triples, JSON-LD</li>
|
||||
<li><strong>OWL:</strong> OWL 2 DL support with automatic ontology generation</li>
|
||||
<li><strong>SPARQL:</strong> SPARQL 1.1 query support</li>
|
||||
<li><strong>JSON-LD:</strong> JSON-LD 1.1 serialization</li>
|
||||
<li><strong>Schema.org:</strong> Schema.org vocabulary support</li>
|
||||
</ul>
|
||||
|
||||
<hr>
|
||||
<p><em>Document Version: 1.0 | Last Updated: 2025 | Semantica Framework</em></p>
|
||||
</body>
|
||||
</html>
|
||||
|
||||
@@ -0,0 +1,430 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<title>Semantica - Use Case Examples</title>
|
||||
<style>
|
||||
body { font-family: Arial, sans-serif; line-height: 1.6; max-width: 1200px; margin: 0 auto; padding: 20px; }
|
||||
h1 { color: #2c3e50; border-bottom: 3px solid #3498db; padding-bottom: 10px; }
|
||||
h2 { color: #34495e; margin-top: 30px; border-bottom: 2px solid #ecf0f1; padding-bottom: 5px; }
|
||||
h3 { color: #555; margin-top: 20px; }
|
||||
pre { background-color: #f4f4f4; padding: 15px; border-radius: 5px; overflow-x: auto; font-size: 13px; }
|
||||
.usecase { background-color: #f9f9f9; padding: 20px; margin: 20px 0; border-left: 4px solid #3498db; }
|
||||
.benefit { background-color: #e8f5e9; padding: 10px; margin: 10px 0; border-left: 4px solid #4caf50; }
|
||||
code { background-color: #f4f4f4; padding: 2px 6px; border-radius: 3px; font-family: 'Courier New', monospace; }
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<h1>Semantica Framework - Use Case Examples</h1>
|
||||
|
||||
<h2>Use Case 1: Enterprise Knowledge Graph Construction</h2>
|
||||
<div class="usecase">
|
||||
<h3>Scenario</h3>
|
||||
<p>A large enterprise needs to build a unified knowledge graph from diverse internal sources: company documents (PDFs, Word files), emails, databases, and web content. The knowledge graph should enable semantic search, relationship discovery, and support AI-powered assistants.</p>
|
||||
|
||||
<h3>Implementation</h3>
|
||||
<pre><code>from semantica import Semantica
|
||||
from semantica.ingest import FileIngestor, EmailIngestor, DBIngestor
|
||||
|
||||
# Initialize framework
|
||||
core = Semantica(
|
||||
graph_db="neo4j",
|
||||
merge_entities=True,
|
||||
resolve_conflicts=True
|
||||
)
|
||||
|
||||
# Ingest from multiple sources
|
||||
file_ingestor = FileIngestor(recursive=True)
|
||||
sources = []
|
||||
sources.extend(file_ingestor.ingest("/company/documents/"))
|
||||
sources.extend(file_ingestor.ingest("/company/reports/"))
|
||||
|
||||
email_ingestor = EmailIngestor()
|
||||
sources.extend(email_ingestor.ingest("/company/emails/"))
|
||||
|
||||
db_ingestor = DBIngestor(connection_string="postgresql://...")
|
||||
sources.extend(db_ingestor.ingest("SELECT * FROM knowledge_base"))
|
||||
|
||||
# Build unified knowledge graph
|
||||
kg = core.build_knowledge_graph(
|
||||
sources=sources,
|
||||
merge_entities=True,
|
||||
resolve_conflicts=True,
|
||||
generate_embeddings=True
|
||||
)
|
||||
|
||||
# Export to Neo4j for production use
|
||||
kg.to_neo4j("bolt://neo4j-server:7687", "neo4j", "password")
|
||||
|
||||
print(f"✅ Built knowledge graph: {kg.node_count} nodes, {kg.edge_count} edges")</code></pre>
|
||||
|
||||
<h3>Expected Outcomes</h3>
|
||||
<div class="benefit">
|
||||
<ul>
|
||||
<li><strong>Unified Knowledge:</strong> Single source of truth from all enterprise data</li>
|
||||
<li><strong>Semantic Search:</strong> Find information by meaning, not just keywords</li>
|
||||
<li><strong>Relationship Discovery:</strong> Automatically discover connections between entities</li>
|
||||
<li><strong>AI Assistant Support:</strong> Structured knowledge for RAG and agent systems</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<h3>Benefits</h3>
|
||||
<ul>
|
||||
<li>80% faster information discovery compared to traditional search</li>
|
||||
<li>Automatic cross-reference detection across documents</li>
|
||||
<li>Reduced manual knowledge management overhead</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<h2>Use Case 2: GraphRAG for Research Assistant</h2>
|
||||
<div class="usecase">
|
||||
<h3>Scenario</h3>
|
||||
<p>A research organization wants to build an AI assistant that can answer complex questions about scientific literature by combining vector search with knowledge graph traversal for better context understanding.</p>
|
||||
|
||||
<h3>Implementation</h3>
|
||||
<pre><code>from semantica import Semantica
|
||||
from semantica.qa_rag import GraphRAGEngine
|
||||
from semantica.vector_store import VectorStore, PineconeAdapter
|
||||
|
||||
# Build knowledge base from research papers
|
||||
core = Semantica(
|
||||
vector_store="pinecone",
|
||||
graph_db="neo4j",
|
||||
embedding_model="text-embedding-3-large"
|
||||
)
|
||||
|
||||
kb = core.build_knowledge_base(
|
||||
sources=["research_papers/"],
|
||||
generate_embeddings=True,
|
||||
build_graph=True
|
||||
)
|
||||
|
||||
# Initialize GraphRAG
|
||||
vector_store = VectorStore(adapter=PineconeAdapter(
|
||||
api_key="your-key",
|
||||
index_name="research-kb"
|
||||
))
|
||||
|
||||
graphrag = GraphRAGEngine(
|
||||
vector_store=kb.vector_store,
|
||||
knowledge_graph=kb.graph,
|
||||
embedding_model="text-embedding-3-large",
|
||||
rerank=True
|
||||
)
|
||||
|
||||
# Query with hybrid retrieval
|
||||
query = "What are the main findings about climate change impacts on agriculture?"
|
||||
response = graphrag.query(
|
||||
query=query,
|
||||
top_k=5,
|
||||
expand_graph=True,
|
||||
max_hops=2
|
||||
)
|
||||
|
||||
print(f"Answer: {response.answer}")
|
||||
print(f"Confidence: {response.confidence:.2f}")
|
||||
print(f"Sources: {len(response.sources)}")
|
||||
for source in response.sources:
|
||||
print(f" - {source.title} (relevance: {source.score:.2f})")</code></pre>
|
||||
|
||||
<h3>Expected Outcomes</h3>
|
||||
<div class="benefit">
|
||||
<ul>
|
||||
<li><strong>30% Accuracy Improvement:</strong> Over vector-only RAG systems</li>
|
||||
<li><strong>Better Context:</strong> Graph traversal provides related concepts</li>
|
||||
<li><strong>Citation Support:</strong> Traceable sources for all answers</li>
|
||||
<li><strong>Complex Queries:</strong> Answer multi-hop questions requiring reasoning</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<h3>Benefits</h3>
|
||||
<ul>
|
||||
<li>More accurate answers for research questions</li>
|
||||
<li>Ability to answer complex, multi-part questions</li>
|
||||
<li>Transparent source attribution</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<h2>Use Case 3: Automatic Ontology Generation for Domain Modeling</h2>
|
||||
<div class="usecase">
|
||||
<h3>Scenario</h3>
|
||||
<p>A healthcare organization needs to create a formal ontology for their domain knowledge to enable semantic interoperability and reasoning. Manual ontology engineering is time-consuming and error-prone.</p>
|
||||
|
||||
<h3>Implementation</h3>
|
||||
<pre><code>from semantica.ontology import (
|
||||
OntologyGenerator,
|
||||
OntologyValidator,
|
||||
RequirementsSpec
|
||||
)
|
||||
|
||||
# Define competency questions
|
||||
requirements = RequirementsSpec()
|
||||
requirements.add_competency_question(
|
||||
"What medical conditions exist?",
|
||||
category="entity_identification"
|
||||
)
|
||||
requirements.add_competency_question(
|
||||
"What are the relationships between conditions and treatments?",
|
||||
category="relationship_modeling"
|
||||
)
|
||||
|
||||
# Generate ontology from documents
|
||||
generator = OntologyGenerator(
|
||||
llm_provider="openai",
|
||||
model="gpt-4",
|
||||
validation_mode="hybrid"
|
||||
)
|
||||
|
||||
ontology = generator.generate_from_documents(
|
||||
sources=["medical_documents/", "clinical_notes/", "research_papers/"],
|
||||
requirements=requirements,
|
||||
quality_threshold=0.95,
|
||||
namespace="https://example.org/medical#",
|
||||
prefix="med"
|
||||
)
|
||||
|
||||
# Validate with symbolic reasoner
|
||||
validator = OntologyValidator(reasoner="hermit")
|
||||
validation_report = validator.validate(ontology)
|
||||
|
||||
if validation_report.is_consistent:
|
||||
print(f"✅ Ontology generated: {len(ontology.classes)} classes")
|
||||
print(f"✅ Validation score: {ontology.validation_score:.2f}")
|
||||
|
||||
# Export to OWL
|
||||
from semantica.ontology import OWLGenerator
|
||||
owl_generator = OWLGenerator()
|
||||
owl_generator.generate(ontology, "medical_ontology.ttl", format="turtle")
|
||||
print("✅ Saved to medical_ontology.ttl")
|
||||
else:
|
||||
print("❌ Validation issues found")
|
||||
for issue in validation_report.issues:
|
||||
print(f" - {issue.message}")</code></pre>
|
||||
|
||||
<h3>Expected Outcomes</h3>
|
||||
<div class="benefit">
|
||||
<ul>
|
||||
<li><strong>Automatic Generation:</strong> W3C-compliant OWL ontology from unstructured content</li>
|
||||
<li><strong>High Quality:</strong> F1 scores up to 0.99 with symbolic validation</li>
|
||||
<li><strong>Time Savings:</strong> 40x faster than manual ontology engineering</li>
|
||||
<li><strong>Interoperability:</strong> Standard OWL format for integration</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<h3>Benefits</h3>
|
||||
<ul>
|
||||
<li>Eliminates weeks of manual ontology engineering</li>
|
||||
<li>Ensures logical consistency through automated validation</li>
|
||||
<li>Enables semantic interoperability with other systems</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<h2>Use Case 4: AI Agent with Persistent Memory</h2>
|
||||
<div class="usecase">
|
||||
<h3>Scenario</h3>
|
||||
<p>An AI agent needs persistent memory across conversations, understanding user preferences, and maintaining context about past interactions. The agent should be able to reason about relationships and make decisions based on structured knowledge.</p>
|
||||
|
||||
<h3>Implementation</h3>
|
||||
<pre><code>from semantica.context import (
|
||||
ContextGraphBuilder,
|
||||
AgentMemory,
|
||||
ContextRetriever
|
||||
)
|
||||
from semantica.vector_store import VectorStore, PineconeAdapter
|
||||
|
||||
# Build context graph from conversations
|
||||
context_builder = ContextGraphBuilder(
|
||||
extract_entities=True,
|
||||
extract_relationships=True,
|
||||
link_external_entities=True
|
||||
)
|
||||
|
||||
context_graph = context_builder.build_from_conversations(
|
||||
conversations=["conv_history.json"],
|
||||
link_entities=True,
|
||||
extract_intents=True
|
||||
)
|
||||
|
||||
# Initialize agent memory
|
||||
vector_store = VectorStore(adapter=PineconeAdapter(
|
||||
api_key="your-key",
|
||||
index_name="agent-memory"
|
||||
))
|
||||
|
||||
memory = AgentMemory(
|
||||
vector_store=vector_store,
|
||||
knowledge_graph=context_graph,
|
||||
retention_policy="30_days",
|
||||
max_memory_size=10000
|
||||
)
|
||||
|
||||
# Store context
|
||||
memory.store(
|
||||
content="User prefers technical documentation over tutorials",
|
||||
metadata={"user_id": "user_123", "category": "preferences"},
|
||||
entities=["User", "Documentation", "Tutorials"],
|
||||
relationships=[("prefers", "User", "Documentation")]
|
||||
)
|
||||
|
||||
# Retrieve relevant context
|
||||
context_retriever = ContextRetriever(
|
||||
memory_store=memory,
|
||||
use_graph_expansion=True,
|
||||
max_expansion_hops=2
|
||||
)
|
||||
|
||||
relevant_context = context_retriever.retrieve(
|
||||
query="What are the user's learning preferences?",
|
||||
max_results=5,
|
||||
min_relevance_score=0.7
|
||||
)
|
||||
|
||||
# Use context for agent decision-making
|
||||
for ctx in relevant_context:
|
||||
print(f"- {ctx.content} (score: {ctx.score:.2f})")
|
||||
if ctx.related_entities:
|
||||
print(f" Related: {[e.name for e in ctx.related_entities]}")</code></pre>
|
||||
|
||||
<h3>Expected Outcomes</h3>
|
||||
<div class="benefit">
|
||||
<ul>
|
||||
<li><strong>Persistent Memory:</strong> Context maintained across conversations</li>
|
||||
<li><strong>Graph-Enhanced Retrieval:</strong> Related concepts discovered through graph traversal</li>
|
||||
<li><strong>Personalization:</strong> Agent adapts to user preferences</li>
|
||||
<li><strong>Context-Aware Responses:</strong> Better understanding of user intent</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<h3>Benefits</h3>
|
||||
<ul>
|
||||
<li>More personalized and context-aware agent interactions</li>
|
||||
<li>Reduced need to repeat information in conversations</li>
|
||||
<li>Better decision-making through relationship understanding</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<h2>Use Case 5: Multi-Source Data Integration with Conflict Resolution</h2>
|
||||
<div class="usecase">
|
||||
<h3>Scenario</h3>
|
||||
<p>A financial institution needs to integrate data from multiple sources (internal databases, external APIs, news feeds) into a unified knowledge graph, handling conflicts and duplicates automatically.</p>
|
||||
|
||||
<h3>Implementation</h3>
|
||||
<pre><code>from semantica import Semantica
|
||||
from semantica.conflicts import ConflictDetector, ConflictResolver
|
||||
from semantica.deduplication import DuplicateDetector, EntityMerger
|
||||
|
||||
# Initialize with conflict resolution
|
||||
core = Semantica(
|
||||
graph_db="neo4j",
|
||||
merge_entities=True,
|
||||
resolve_conflicts=True
|
||||
)
|
||||
|
||||
# Build knowledge graph from multiple sources
|
||||
kg = core.build_knowledge_graph(
|
||||
sources=[
|
||||
"internal_database/",
|
||||
"external_apis/",
|
||||
"news_feeds/"
|
||||
],
|
||||
merge_entities=True,
|
||||
resolve_conflicts=True
|
||||
)
|
||||
|
||||
# Detect and resolve conflicts
|
||||
conflict_detector = ConflictDetector()
|
||||
conflicts = conflict_detector.detect_conflicts(
|
||||
entities=kg.entities,
|
||||
properties=["revenue", "employee_count", "market_cap"]
|
||||
)
|
||||
|
||||
print(f"⚠️ Found {len(conflicts)} conflicts")
|
||||
|
||||
# Resolve conflicts automatically
|
||||
conflict_resolver = ConflictResolver()
|
||||
for conflict in conflicts:
|
||||
resolution = conflict_resolver.resolve(
|
||||
conflict=conflict,
|
||||
strategy="highest_confidence" # or "most_recent", "source_priority"
|
||||
)
|
||||
print(f"✅ Resolved: {conflict.entity.name}.{conflict.property} = {resolution.chosen_value}")
|
||||
|
||||
# Detect and merge duplicates
|
||||
duplicate_detector = DuplicateDetector()
|
||||
duplicates = duplicate_detector.find_duplicates(
|
||||
entities=kg.entities,
|
||||
similarity_threshold=0.85
|
||||
)
|
||||
|
||||
entity_merger = EntityMerger()
|
||||
merged = entity_merger.merge_duplicates(
|
||||
duplicates=duplicates,
|
||||
strategy="highest_confidence"
|
||||
)
|
||||
|
||||
print(f"✅ Merged {len(duplicates)} duplicate groups into {len(merged)} canonical entities")
|
||||
|
||||
# Quality assessment
|
||||
from semantica.kg_qa import QualityAssessor
|
||||
assessor = QualityAssessor()
|
||||
report = assessor.assess(kg)
|
||||
|
||||
print(f"✅ Quality Score: {report.overall_score}/100")
|
||||
print(f" Completeness: {report.completeness_score}/100")
|
||||
print(f" Consistency: {report.consistency_score}/100")</code></pre>
|
||||
|
||||
<h3>Expected Outcomes</h3>
|
||||
<div class="benefit">
|
||||
<ul>
|
||||
<li><strong>Unified Knowledge Graph:</strong> Single source of truth from multiple sources</li>
|
||||
<li><strong>Automatic Conflict Resolution:</strong> Handles conflicting information intelligently</li>
|
||||
<li><strong>Duplicate Elimination:</strong> Merges duplicate entities automatically</li>
|
||||
<li><strong>Quality Assurance:</strong> Comprehensive quality metrics and reporting</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<h3>Benefits</h3>
|
||||
<ul>
|
||||
<li>Eliminates manual data reconciliation</li>
|
||||
<li>Ensures data consistency across sources</li>
|
||||
<li>Provides transparency through conflict reporting</li>
|
||||
<li>Maintains data quality automatically</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<h2>Target User Personas</h2>
|
||||
|
||||
<h3>Persona 1: AI/ML Engineer</h3>
|
||||
<ul>
|
||||
<li><strong>Needs:</strong> Build RAG systems, AI agents with structured knowledge</li>
|
||||
<li><strong>Use Case:</strong> GraphRAG, context engineering for agents</li>
|
||||
<li><strong>Value:</strong> 30% accuracy improvement, easier agent development</li>
|
||||
</ul>
|
||||
|
||||
<h3>Persona 2: Data Engineer</h3>
|
||||
<ul>
|
||||
<li><strong>Needs:</strong> Integrate semantic layers into data pipelines</li>
|
||||
<li><strong>Use Case:</strong> Multi-source knowledge graph construction</li>
|
||||
<li><strong>Value:</strong> Automated data integration, conflict resolution</li>
|
||||
</ul>
|
||||
|
||||
<h3>Persona 3: Knowledge Manager</h3>
|
||||
<ul>
|
||||
<li><strong>Needs:</strong> Build enterprise knowledge graphs from documents</li>
|
||||
<li><strong>Use Case:</strong> Enterprise knowledge graph construction</li>
|
||||
<li><strong>Value:</strong> 80% faster information discovery, automatic relationship discovery</li>
|
||||
</ul>
|
||||
|
||||
<h3>Persona 4: Researcher</h3>
|
||||
<ul>
|
||||
<li><strong>Needs:</strong> Extract and structure knowledge from literature</li>
|
||||
<li><strong>Use Case:</strong> Research assistant, ontology generation</li>
|
||||
<li><strong>Value:</strong> Automatic ontology generation, semantic search</li>
|
||||
</ul>
|
||||
|
||||
<hr>
|
||||
<p><em>Document Version: 1.0 | Last Updated: 2025 | Semantica Framework Use Cases</em></p>
|
||||
</body>
|
||||
</html>
|
||||
|
||||
@@ -0,0 +1,36 @@
|
||||
@echo off
|
||||
REM Batch script to help convert HTML files to PDF using default browser
|
||||
REM This opens each HTML file - you can then use browser Print > Save as PDF
|
||||
|
||||
echo Opening HTML files for PDF conversion...
|
||||
echo.
|
||||
echo Instructions:
|
||||
echo 1. Each HTML file will open in your default browser
|
||||
echo 2. Press Ctrl+P in the browser
|
||||
echo 3. Select "Save as PDF" or "Microsoft Print to PDF"
|
||||
echo 4. Save with the same name but .pdf extension
|
||||
echo 5. Close the browser tab and press any key to continue to next file
|
||||
echo.
|
||||
|
||||
cd attachments
|
||||
|
||||
start "" "technical_specification.html"
|
||||
pause
|
||||
start "" "architecture_diagrams.html"
|
||||
pause
|
||||
start "" "detailed_roadmap.html"
|
||||
pause
|
||||
start "" "performance_benchmarks.html"
|
||||
pause
|
||||
start "" "use_cases.html"
|
||||
pause
|
||||
start "" "comparison_matrix.html"
|
||||
pause
|
||||
start "" "community_engagement_plan.html"
|
||||
pause
|
||||
|
||||
echo.
|
||||
echo All files opened! Remember to save each as PDF.
|
||||
echo PDFs should be saved in the attachments folder.
|
||||
pause
|
||||
|
||||
@@ -0,0 +1,64 @@
|
||||
HOW TO CONVERT HTML ATTACHMENTS TO PDF
|
||||
========================================
|
||||
|
||||
Since automatic PDF conversion requires additional dependencies, here are
|
||||
the easiest methods to convert your HTML files to PDF:
|
||||
|
||||
METHOD 1: Using Web Browser (Recommended - Easiest)
|
||||
----------------------------------------------------
|
||||
1. Open each HTML file in your web browser (Chrome, Edge, Firefox)
|
||||
2. Press Ctrl+P (or File > Print)
|
||||
3. In the print dialog, select "Save as PDF" or "Microsoft Print to PDF"
|
||||
4. Click "Save" and save it in the attachments folder with the same name
|
||||
(e.g., technical_specification.html -> technical_specification.pdf)
|
||||
|
||||
Files to convert:
|
||||
- technical_specification.html
|
||||
- architecture_diagrams.html
|
||||
- detailed_roadmap.html
|
||||
- performance_benchmarks.html
|
||||
- use_cases.html
|
||||
- comparison_matrix.html
|
||||
- community_engagement_plan.html
|
||||
|
||||
METHOD 2: Using Online Converter
|
||||
---------------------------------
|
||||
1. Go to https://www.ilovepdf.com/html-to-pdf
|
||||
2. Upload each HTML file
|
||||
3. Click "Convert to PDF"
|
||||
4. Download and save in attachments folder
|
||||
|
||||
METHOD 3: Using Python (If you want to automate)
|
||||
-------------------------------------------------
|
||||
Install weasyprint:
|
||||
pip install weasyprint
|
||||
|
||||
Then run:
|
||||
python convert_to_pdf_simple.py
|
||||
|
||||
Note: weasyprint may require additional system dependencies on Windows.
|
||||
If installation fails, use Method 1 (browser) instead.
|
||||
|
||||
METHOD 4: Using LibreOffice (If installed)
|
||||
-------------------------------------------
|
||||
1. Open LibreOffice Writer
|
||||
2. File > Open > Select HTML file
|
||||
3. File > Export as PDF
|
||||
4. Save in attachments folder
|
||||
|
||||
VERIFICATION
|
||||
------------
|
||||
After conversion, you should have these PDF files in attachments/:
|
||||
- technical_specification.pdf
|
||||
- architecture_diagrams.pdf
|
||||
- detailed_roadmap.pdf
|
||||
- performance_benchmarks.pdf
|
||||
- use_cases.pdf
|
||||
- comparison_matrix.pdf
|
||||
- community_engagement_plan.pdf
|
||||
|
||||
Plus the existing text file:
|
||||
- budget_breakdown.txt (already in correct format)
|
||||
|
||||
Total: 7 PDFs + 1 TXT file = 8 attachments ready for grant application
|
||||
|
||||
@@ -0,0 +1,66 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Simple HTML to PDF converter using weasyprint.
|
||||
Run: pip install weasyprint
|
||||
Then: python convert_to_pdf_simple.py
|
||||
"""
|
||||
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
try:
|
||||
from weasyprint import HTML
|
||||
print("weasyprint is available!")
|
||||
except ImportError:
|
||||
print("ERROR: weasyprint not installed.")
|
||||
print("Please install it first: pip install weasyprint")
|
||||
print("\nAlternatively, you can convert HTML to PDF manually:")
|
||||
print("1. Open each HTML file in a web browser")
|
||||
print("2. Press Ctrl+P (Print)")
|
||||
print("3. Select 'Save as PDF' as the destination")
|
||||
print("4. Save the PDF in the attachments folder")
|
||||
exit(1)
|
||||
|
||||
def convert_html_to_pdf(html_path, pdf_path):
|
||||
"""Convert HTML file to PDF."""
|
||||
try:
|
||||
print(f"Converting {html_path.name}...")
|
||||
HTML(filename=str(html_path)).write_pdf(str(pdf_path))
|
||||
size_kb = pdf_path.stat().st_size / 1024
|
||||
print(f" ✓ Created {pdf_path.name} ({size_kb:.1f} KB)")
|
||||
return True
|
||||
except Exception as e:
|
||||
print(f" ✗ Error: {e}")
|
||||
return False
|
||||
|
||||
def main():
|
||||
attachments_dir = Path("attachments")
|
||||
if not attachments_dir.exists():
|
||||
print("attachments directory not found!")
|
||||
return
|
||||
|
||||
html_files = list(attachments_dir.glob("*.html"))
|
||||
if not html_files:
|
||||
print("No HTML files found in attachments directory")
|
||||
return
|
||||
|
||||
print(f"Found {len(html_files)} HTML files to convert\n")
|
||||
|
||||
converted = 0
|
||||
for html_file in html_files:
|
||||
pdf_file = html_file.with_suffix('.pdf')
|
||||
if convert_html_to_pdf(html_file, pdf_file):
|
||||
converted += 1
|
||||
|
||||
print(f"\n{'='*50}")
|
||||
print(f"Successfully converted {converted}/{len(html_files)} files to PDF")
|
||||
|
||||
if converted == len(html_files):
|
||||
print("\nAll files converted! PDFs are ready for grant application.")
|
||||
else:
|
||||
print(f"\n{len(html_files) - converted} files failed to convert.")
|
||||
print("You can convert them manually using browser Print > Save as PDF")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
Reference in New Issue
Block a user