Image Classification and RDF Graph Generation with ConceptNet and YOLO

Project Overview

This project focuses on building an Image Classification pipeline combined with RDF Graph Generation using ConceptNet and YOLO. The goal is to create a system that classifies images, links the results to semantic data, and visualizes the outcomes in an interactive way.

Features

• Image Classification with YOLO:
  Classified input images using YOLO (You Only Look Once) for object detection and labelling.

• Semantic Enrichment with ConceptNet:
  Queried the ConceptNet knowledge graph via SPARQL to retrieve semantic relationships between detected objects in the images.

• RDF Graph Construction:
  Constructed enriched RDF graphs, combining image metadata (such as labels and detected objects) with external knowledge from ConceptNet.

• HTML Visualization:
  Delivered an HTML-based visualization of the RDF graphs, including both graphical and tabular representations of the data.

• Knowledge-Driven Insights:
  Integrated image classification and semantic enrichment to enable knowledge-driven insights and provide meaningful visualizations.