Ontology-Driven Geological QA System

A Flask-based Geological Ontology Question Answering System that enables users to query geological knowledge using natural language. This project, Ontology-Driven QA, demonstrates a question-answering system for Geological ontology. It includes Python scripts, front-end templates, and deployment instructions for a complete solution. This system enables users to query geological knowledge using natural language.

Table of contents

1. Overview
2. Features
3. Technical Architecture
   • Tools & Technologies
   • Pipeline Overview
4. How to Use
5. Results
6. Future Work

Overview

This project, Ontology-Driven QA, demonstrates a question-answering system for geological ontologies. It integrates Flask as a lightweight web framework, enabling users to query geological knowledge using natural language through a simple and intuitive web interface.

By dynamically extracting and processing geological ontology structures containing over 1600 axioms, this system efficiently answers user queries with relevant results using SPARQL and NLP-powered term matching.

Features

• Flask Web Interface: A user-friendly web application that allows users to input natural language queries and retrieve geological knowledge seamlessly.

• Ontology-Driven QA: Supports querying complex geological relationships and descriptions from RDF/OWL files using SPARQL.

• Dynamic Ontology Processing: Extracts ontology classes, relationships, and descriptions dynamically for maximum flexibility.

• NLP Integration: Preprocesses and matches user queries with ontology terms using spaCy and PhraseMatcher.

• Efficient Query Optimization: Caches frequently queried data and implements preprocessing to reduce SPARQL execution times.

Technical Architecture

Tools & Technologies

• Flask: For building the web interface and deploying the application.
• Python: Core programming language for ontology extraction and query processing.
• RDFLib: Parsing and querying RDF graphs using SPARQL.
• spaCy: NLP pipeline for preprocessing and term matching.
• SPARQL: For retrieving geological knowledge from the ontology.

Pipeline Overview

• Flask Web Application

  • A responsive interface where users can input natural language queries to retrieve geological insights.

• Ontology Parsing

  • Dynamically parses RDF/OWL ontology files to extract classes, relationships, and instance descriptions.

• NLP Preprocessing

  • Preprocesses user queries and ontology terms using lemmatization and tokenization to improve accuracy.

• SPARQL Query Execution

  • Matches processed queries to ontology terms and retrieves relevant knowledge using SPARQL queries.

• Efficient Query Optimization

  • Reduces initialization and query processing times using caching and multi-threaded processing.

How to Use

You can access the applictaion using this Geological Ontology Question Answering System or https://ontology-driven-qa.onrender.com/.

Results

• Built a Flask-based geological QA system that handles natural language queries and retrieves relevant ontology knowledge.
• Extracted and structured 1600+ axioms dynamically from an RDF-based ontology.
• Reduced query initialization times by up to 50% using multi-threading and efficient caching.
• Designed a reusable, scalable framework suitable for any domain-specific ontology.

Future Work

• Add support for more complex queries involving multiple relationships.
• Develop a dashboard visualization for the query results.
• Experiment with other advanced NLP models.
• Extend support to handle multiple ontologies in parallel.