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RDF Data Graphs: Property and Hyper Data Modeling using Microsoft GraphEngine TSL, Computed DSL, LIKQ and APIs

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InKnowWorks/RDF-Graph-and-Hypergraph

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A comprehensive resource collection for semantic knowledge graph engineering using Microsoft Trinity Graph Engine (TGE) with formal ontology integration.

📚 Resources & Publications

New Technical White Papers

Developer Guides

Book in Progress

  • Microsoft Graph Engine Developer's Guide (First 4 chapters nearly complete)
    A comprehensive guide for developers looking to master Graph Engine for knowledge graph applications.

🔬 Exploration Areas

Graph Theory & Implementation

  • RDF Triple & Graph Theory and Practice

    • Core concepts and implementation patterns
    • Performance optimization strategies
    • Distributed graph processing
  • HyperGraph & MultiGraph Models

    • Hypergraph theory and implementation with TSL
    • Property graph modeling techniques
    • Multi-graph traversal and query optimization

Data Modeling & Representation

  • TSL Data Modeling

    • RDF/RDFS representation in TSL
    • Attribute-based metadata tagging
    • Integration with predicate logic (First-order, Second-order)
    • Guan Logic Programming SDK integration
  • Ontology Engineering

    • BFO 2020 Upper Ontology integration with TSL
    • OREO Upper Ontology mapping
    • IAO (Information Artifact Ontology) implementation
    • SKOS, OWL 2 DL, and RDF/RDFS modeling
    • Custom People & Contact ontologies based on BFO/IAO

Query & Traversal Techniques

  • LIKQ (Language Integrated Knowledge Query)

    • Client-side vs. Server-side implementation
    • Design patterns and best practices
    • Comparison with SPARQL/SHACL
    • Performance optimization strategies
  • Graph Traversal Algorithms

    • BFS, DFS, and backtracking implementations
    • Type-constrained path finding
    • Semantic reasoning using LIKQ with ontologies
    • Pattern mining and motif detection

Advanced Programming Patterns

  • Generic Programming with Graph Engine

    • Server-side generic implementations
    • Attribute-based markup for semantic enrichment
    • Graph Engine Adapters using Graph Engine Generics
  • Reactive Programming Integration

    • Symmetric reactive eventing
    • Integration with ReactiveX, MessagePipe, Orleans, and Akka.NET
    • Push automation techniques

Architecture & Deployment

  • Graph Engine Runtime Management

    • Hosting options and deployment models
    • Scaling strategies for enterprise deployments
    • Memory management and optimization
    • Distributed processing techniques
  • Integration Patterns

    • Legacy data ingestion automation
    • Symmetric networking with reactive callbacks
    • External system integration strategies

💻 Demo Solutions & Code Examples

This repository contains multiple demo solutions created with Visual Studio 2022, using C# 10.x:

  • Console applications
  • WPF/WinUI/MAUI projects
  • Server components

All examples center around semantic modeling in the Real Estate domain, demonstrating:

  • Ontology-driven data modeling
  • Complex graph traversal techniques
  • Semantic reasoning with formal ontologies
  • UI integration for knowledge graph visualization and interaction

🔧 Getting Started

  1. Clone this repository
  2. Install prerequisites (.NET 9.0, Visual Studio 2022)
  3. Explore the demo solutions in the/Demos directory
  4. Refer to the Developer Setup Guide for environment configuration
  5. Review the technical white papers for conceptual background

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

📧 Contact

For technical support, implementation assistance, or additional information about integrating Trinity Graph Engine with formal ontologies, please contact the author attavi@rocketurbiz.com or visitwww.rocketurbiz.com.

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RDF Data Graphs: Property and Hyper Data Modeling using Microsoft GraphEngine TSL, Computed DSL, LIKQ and APIs

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