17 KiB
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:
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Comprehensive Semantic Layer: First open-source framework providing end-to-end semantic intelligence from ingestion to AI application integration, not just individual components.
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Automatic Ontology Generation: Unique 6-stage LLM-based pipeline generating W3C-compliant ontologies from unstructured content with symbolic validation—no manual ontology engineering required.
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Production-Grade Quality Assurance: Built-in conflict detection, deduplication, schema enforcement, and seed data systems—capabilities missing in academic or prototype tools.
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GraphRAG Integration: Native hybrid retrieval combining vector similarity with graph traversal, achieving 30% accuracy improvements over vector-only RAG.
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Multi-Source Knowledge Engineering: Handles 50+ formats with automatic entity resolution, conflict resolution, and provenance tracking across heterogeneous sources.
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Open Standards Compliance: Full support for RDF, OWL, SPARQL, JSON-LD with export to standard formats, ensuring interoperability.
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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:
- AI/ML Engineers: Building RAG systems, AI agents, and multi-agent systems requiring context-aware knowledge
- Data Engineers: Integrating semantic layers into data pipelines and knowledge management systems
- Researchers: Academic and industry researchers working on knowledge engineering, semantic web, and AI applications
- Enterprise Teams: Organizations building internal knowledge graphs, semantic search, and intelligent document processing
- 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