SER531 Group 7 - IPL Insights: A Semantic Ontology Approach for Data Querying and Match Prediction

This project leverages semantic ontology to analyze and query IPL cricket data, enabling advanced insights such as performance analysis, player impact in high-pressure games, and venue-specific trends. By integrating a React-based frontend, a Python backend, and an Apache Jena Fuseki server, it provides a unified platform for intelligent data querying and reasoning over complex cricket datasets.

Table of Contents

• Project Overview
• Setup Instructions
• Usage
• Technologies Used

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Project Overview

The SER531 Group 7 Data Integration Project demonstrates semantic data integration using Apache Jena Fuseki for RDF data handling. The system includes a Python backend for processing data and a React.js frontend for user interactions.

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Setup Instructions

1. Clone the Repository

Clone the repository to your local machine:

git clone https://github.com/mdeshp11/SER531-Group-7-Data-Integration-Project.git
cd SER531-Group-7-Data-Integration-Project

2. Backend Setup

Follow these steps to set up the backend:

1. Install the required Python packages:

   pip install -r requirements.txt

2. Navigate to the Backend folder:

   cd Backend

3. Start the backend server:

   python app.py

3. Apache Jena Fuseki Server Setup

Set up the Apache Jena Fuseki server by following these steps:

1. Download the Apache Jena Fuseki server from the official site: [Apache Jena Fuseki 5.2.0] (https://jena.apache.org/documentation/fuseki2/)
2. Extract the downloaded .zip file.
3. Navigate to the extracted folder.
4. Start the Fuseki server on port 8080:

   fuseki-server --port=8080

4. Frontend Setup

Set up the frontend application with the following steps:

1. Navigate to the Frontend folder:

   cd ../Frontend

2. Install the required dependencies:

   npm install

3. Start the React application:

   npm start

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Usage

1. Ensure that the backend server, Apache Jena Fuseki server, and frontend application are running.
2. Access the application in your web browser at:

   http://localhost:3000

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Technologies Used

• Backend: Python, Flask
• Frontend: React.js
• Semantic Data Integration: Apache Jena Fuseki Server
• Dataset preprocessing: Google Sheets, Pandas
• Knowledge graph: Ontotext Refine

Design Changes

The ontology development process now includes mapping raw cricket data from CSV files to RDF triples using specific predicates like smw:hasBatter and smw:hasBowler, ensuring seamless integration with Apache Jena Fuseki. This process is further explained with algorithms detailing how attributes from datasets are transformed into ontology-compatible formats. Additionally, reasoning capabilities have been refined to infer new knowledge from existing relationships. For instance, a player’s dismissal can now be dynamically linked to their performance in a match. The OntoGraf plugin in Protégé has been leveraged to visually represent these enhanced relationships, highlighting the interconnectedness of classes like Match, Player, Venue, and their respective properties.