ontology-semantic-map-with-llm

Ontology-Based Game Recommendation & Query System

Overview

This project consists of two phases that work together to provide an ontology-driven game recommendation system with a chatbot for querying data using SPARQL.

• First Phase: A Flask-based web application that utilizes RDF and OWL ontology for game recommendations.
• Second Phase: A chatbot interface powered by the Open router API allowing users to query ontology data in natural language.

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First Phase: Ontology-Based Game Recommendation

Description

The first phase of this project is a Flask web application that uses an OWL ontology file to filter and recommend games based on user-selected criteria, such as platform, genre, and difficulty. It utilizes SPARQL queries to extract relevant game recommendations.

Setup Instructions

1. Clone the Repository

   https://github.com/satilmiskabasakal0/ontology-semantic-map-with-llm.git
   cd /ontology-semantic-map-with-llm

2. Install Dependencies

   pip install -r requirements.txt

3. Run the Application

   python first_phase.py

   The application will run at http://127.0.0.1:5051/

4. Usage

   • The web interface allows users to select game preferences.
   • The system will filter and return relevant game recommendations based on the ontology data.

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Second Phase: SPARQL Query Chatbot with OpenRouter API

Description

The second phase extends the functionality of the first phase by integrating a chatbot powered by the OpenRouter API. This chatbot allows users to interact with the ontology-based knowledge system using natural language queries.

Prerequisites

OpenRouter API Key

To use the chatbot, you need to obtain an OpenRouter API key from OpenRouter's website. Once obtained, create a .env file in the project directory and add your API key:

Setup Instructions

1. Install Dependencies

   pip install -r requirements.txt

2. Run the Application

   python second_phase.py

   The chatbot will be accessible at http://127.0.0.1:5050/

3. Usage

   • The chatbot allows users to ask questions based on the ontology knowledge base.
   • The bot provides relevant answers using the Groq API.

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Project Structure

/
│── first_phase.py         # Flask application for game recommendation
│── second_phase.py        # Flask chatbot with Groq API integration
│── json-ontology.jsonld   # Ontology file
│── dummytext.txt          # Knowledge base for chatbot
│── .env                   # API key storage (add manually)
│── requirements.txt       # Python dependencies

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Notes

• Ensure that the ontology file json-ontology.jsonld is correctly formatted and available in the project directory.
• For the second phase, the chatbot relies on dummytext.txt as a static knowledge base.
• Modify dummytext.txt to include domain-specific information for better responses.
• You can deploy both phases separately or integrate them within a single application.