---
title: Cars Knowledge Graph
emoji: 🚗
colorFrom: blue
colorTo: red
sdk: docker
pinned: false
---

# Knowledge Representation Project: Cars Knowledge Graph

## 1. Introduction & Motivation
This project transforms a flat CSV dataset of **Car Specifications (2025)** into a semantic **Knowledge Graph (RDF/OWL)**. 
**Domain**: Automotive Industry (Cars, Manufacturers, Engines, Performance).
**Motivation**: To enable complex querying of car data that is not possible with simple tabular lookups, such as inferring "High Performance" vehicles or categorizing cars by complex criteria (e.g., specific engine types + price ranges).

## 2. Ontology Design (Conceptual Model)
The Ontology is designed using **RDFLib** and conforms to the project requirements (20+ Classes, 7+ Properties).

### Core Classes
- **Car**: The central entity.
  - **Subclasses**: `SportsCar`, `HyperCar`, `Sedan`, `Coupe`, `ElectricCar`, `PetrolCar`, `LuxuryCar`.
- **Manufacturer**: Companies like Ferrari, Tesla, Bugatti.
- **Component**: `Engine`, `V8Engine`, `V12Engine`.
- **defined Classes**: 
  - `SuperCar`: Cars with Top Speed > 300 km/h.

### Properties
- **Object Properties**: `hasManufacturer`, `hasEngine`, `usesFuel`, `manufactures`.
- **Datatype Properties**: `hasPriceValue`, `hasHorsePowerValue`, `hasSeatCount`, `hasTopSpeedKMH`.

## 3. Knowledge Graph Construction
- **Source**: `Cars Datasets 2025.csv`
- **Output**: `cars_knowledge_graph.ttl` (Turtle Syntax)
- **Triples Generated**: ~9,842

## 4. Competency Questions & Validation
The following questions guided the design and were validated via SPARQL:

1. **"List all cars manufactured by Ferrari"**
   - Verified: Returns models like `SF90 STRADALE`, `ROMA`, `812 GTS`.
2. **"Which cars have > 800 HorsePower?"**
   - Verified: Returns `Bugatti Chiron`, `Tesla Roadster 2`, etc.
3. **"Count of 2-Seater Coupes"**
   - Result: 147 vehicles.
4. **"What is the average price of all cars?"**
   - Result: ~$137,193 USD.

## 5. How to Run
### Prerequisites
- Python 3.x
- Libraries: `rdflib`, `pandas`

### Steps
1. **Install Dependencies**:
   ```bash
   pip install rdflib pandas
   ```
2. **Generate Knowledge Graph**:
   ```bash
   python src/ontology.py      # Generates Ontology Schema
   python src/convert_data.py  # Generates Graph from CSV
   ```
3. **Run Validation Queries**:
   ```bash
   python src/validate.py
   ```