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| title: "Ontology-Enhanced RAG System" | |
| emoji: "🧠" | |
| colorFrom: "indigo" | |
| colorTo: "blue" | |
| sdk: "streamlit" | |
| sdk_version: "1.44.0" | |
| app_file: "app.py" | |
| pinned: false | |
| # Enhanced Ontology-RAG System | |
| ## Project Overview | |
| This repository contains an advanced Retrieval-Augmented Generation (RAG) system that integrates structured ontologies with language models. The system demonstrates how formal ontological knowledge representation can enhance traditional vector-based retrieval methods to provide more accurate, contextually rich, and logically consistent answers to user queries. | |
| The project implements a sophisticated architecture that combines: | |
| - JSON-based ontology representation with classes, relationships, rules, and instances | |
| - Knowledge graph visualization for exploring entity relationships | |
| - Semantic path finding for multi-hop reasoning between concepts | |
| - Comparative analysis between traditional vector-based RAG and ontology-enhanced RAG | |
| The application is built with **Streamlit** for the frontend interface, uses **FAISS** for vector embeddings, **NetworkX** for graph representation, and integrates with **OpenAI's language models** for generating responses. | |
| ## Key Features | |
| 1. **RAG Comparison Demo** | |
| - Side-by-side comparison of traditional and ontology-enhanced RAG | |
| - Analysis of differences in answers and retrieved context | |
| 2. **Knowledge Graph Visualization** | |
| - Interactive network graph for exploring the ontology structure | |
| - Multiple layout algorithms (force-directed, hierarchical, radial, circular) | |
| - Entity relationship exploration with customizable focus | |
| 3. **Ontology Structure Analysis** | |
| - Visualization of class hierarchies and statistics | |
| - Relationship usage and domain-range distribution analysis | |
| - Graph statistics including node counts, edge counts, and centrality metrics | |
| 4. **Entity Exploration** | |
| - Detailed entity information cards showing properties and relationships | |
| - Relationship graphs centered on specific entities | |
| - Neighborhood exploration for entities | |
| 5. **Semantic Path Visualization** | |
| - Path visualization between entities with step-by-step explanation | |
| - Visual representation of paths through the knowledge graph | |
| - Connection to relevant business rules | |
| 6. **Reasoning Trace Visualization** | |
| - Query analysis with entity and relationship detection | |
| - Sankey diagrams showing information flow in the RAG process | |
| - Explanation of reasoning steps | |
| ## Ontology Structure Example | |
| The `data/enterprise_ontology.json` file contains a rich enterprise ontology that models organizational knowledge. Here's a breakdown of its key components: | |
| ### Classes (Entity Types) | |
| The ontology defines a hierarchical class structure with inheritance relationships. For example: | |
| - **Entity** (base class) | |
| - **FinancialEntity** → Budget, Revenue, Expense | |
| - **Asset** → PhysicalAsset, DigitalAsset, IntellectualProperty | |
| - **Person** → InternalPerson → Employee → Manager | |
| - **Process** → BusinessProcess, DevelopmentProcess, SupportProcess | |
| - **Market** → GeographicMarket, DemographicMarket, BusinessMarket | |
| Each class has a description and a set of defined properties. For instance, the `Employee` class includes properties like role, hire date, and performance rating. | |
| ### Relationships | |
| The ontology defines explicit relationships between entity types, including: | |
| - `ownedBy`: Connects Product to Department | |
| - `managedBy`: Connects Department to Manager | |
| - `worksOn`: Connects Employee to Product | |
| - `purchases`: Connects Customer to Product | |
| - `provides`: Connects Customer to Feedback | |
| - `optimizedBy`: Relates Product to Feedback | |
| Each relationship has metadata such as domain, range, cardinality, and inverse relationship name. | |
| ### Business Rules | |
| The ontology contains formal business rules that constrain the knowledge model: | |
| - "Every Product must be owned by exactly one Department" | |
| - "Every Department must be managed by exactly one Manager" | |
| - "Critical support tickets must be assigned to Senior employees or managers" | |
| - "Product Lifecycle stages must follow a predefined sequence" | |
| ### Instances | |
| The ontology includes concrete instances of the defined classes, such as: | |
| - `product1`: An "Enterprise Analytics Suite" owned by the Engineering department | |
| - `manager1`: A director named "Jane Smith" who manages the Engineering department | |
| - `customer1`: "Acme Corp" who has purchased product1 and provided feedback | |
| Each instance has properties and relationships to other instances, forming a connected knowledge graph. | |
| This structured knowledge representation allows the system to perform semantic reasoning beyond what would be possible with simple text-based approaches, enabling it to answer complex queries that require understanding of hierarchical relationships, business rules, and multi-step connections between entities. | |
| ## Getting Started | |
| ### Prerequisites | |
| - Python 3.8+ | |
| - OpenAI API key | |
| ### Installation | |
| 1. Clone this repository | |
| 2. Install the required dependencies: | |
| ``` | |
| pip install -r requirements.txt | |
| ``` | |
| 3. Set up your OpenAI API key as an environment variable or in the Streamlit secrets | |
| ### Running the Application | |
| To run the application locally: | |
| ``` | |
| streamlit run app.py | |
| ``` | |
| For deployment instructions, please refer to the [DEPLOYMENT_GUIDE.md](DEPLOYMENT_GUIDE.md). | |
| ## Project Structure | |
| ``` | |
| ontology-rag/ | |
| ├── .streamlit/ | |
| │ └── config.toml # Streamlit configuration | |
| ├── data/ | |
| │ └── enterprise_ontology.json # Enterprise ontology data | |
| │ └── enterprise_ontology.txt # Simplified text representation of ontology | |
| ├── src/ | |
| │ ├── __init__.py | |
| │ ├── knowledge_graph.py # Knowledge graph processing | |
| │ ├── ontology_manager.py # Ontology management | |
| │ ├── semantic_retriever.py # Semantic retrieval | |
| │ └── visualization.py # Visualization functions | |
| ├── static/ | |
| │ └── css/ | |
| │ └── styles.css # Custom styles | |
| ├── app.py # Main application | |
| ├── requirements.txt # Dependencies list | |
| ├── DEPLOYMENT_GUIDE.md # Deployment instructions | |
| └── README.md # This file | |
| ``` | |
| ## Use Cases | |
| ### Enterprise Knowledge Management | |
| The ontology-enhanced RAG system can help organizations effectively organize and access their knowledge assets, connecting information across different departments and systems to provide more comprehensive business insights. | |
| ### Product Development Decision Support | |
| By understanding the relationships between customer feedback, product features, and market data, the system can provide more valuable support for product development decisions. | |
| ### Complex Compliance Queries | |
| In compliance scenarios where multiple rules and relationships need to be considered, the ontology-enhanced RAG can provide rule-based reasoning to ensure recommendations comply with all applicable policies and regulations. | |
| ### Diagnostics and Troubleshooting | |
| In technical support and troubleshooting scenarios, the system can connect symptoms, causes, and solutions through multi-hop reasoning to provide more accurate diagnoses. | |
| ## Acknowledgments | |
| This project demonstrates the integration of ontological knowledge with RAG systems for enhanced query answering capabilities. It builds upon research in knowledge graphs, semantic web technologies, and large language models. | |
| ## License | |
| This project is licensed under the MIT License - see the license file for details. | |
| ## Reference: | |
| Sharma, K., Kumar, P. and Li, Y., 2024. OG-RAG: Ontology-Grounded Retrieval-Augmented Generation For Large Language Models. arXiv preprint arXiv:2412.15235. Available at: https://arxiv.org/abs/2412.15235 [Accessed 16 Apr. 2025]. |