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Shreyas Devaraj
Table of Contents

Abstract

This research presents a comprehensive approach to developing a Question Answering System using Knowledge Graphs, specifically designed for educational personalization. The system demonstrates advanced capabilities in multi-hop reasoning and information retrieval, making it particularly effective for educational applications.

Authors

S. Skandan, S. Kanungo, S. Devaraj, et al.

Publication Details

Key Contributions

1. Multi-hop Question Answering

Our system implements sophisticated multi-hop reasoning capabilities, allowing it to answer complex questions that require connecting multiple pieces of information across a knowledge graph. This approach significantly improves the quality and depth of responses compared to traditional single-hop systems.

2. Educational Personalization

The system is specifically tailored for educational contexts, with features that:

  • Adapt explanations based on student level
  • Provide contextual learning paths
  • Support progressive difficulty scaling
  • Enable personalized content delivery

3. Knowledge Graph Integration

We leverage structured knowledge representations to:

  • Maintain semantic relationships between concepts
  • Enable efficient traversal of related information
  • Support complex query resolution
  • Provide explainable reasoning paths

Technical Implementation

The system architecture incorporates:

  • Graph Neural Networks for knowledge representation learning
  • Natural Language Processing for query understanding
  • Multi-hop reasoning algorithms for complex question resolution
  • Web deployment framework for practical accessibility

Applications and Impact

This research has significant implications for:

Educational Technology

  • Intelligent tutoring systems
  • Adaptive learning platforms
  • Automated assessment tools
  • Personalized curriculum design

Information Retrieval

  • Advanced search systems
  • Knowledge base querying
  • Semantic information extraction
  • Context-aware recommendations

Future Work

The research opens several avenues for future exploration:

  • Integration with large language models
  • Real-time learning and knowledge graph updates
  • Multi-modal question answering capabilities
  • Scalability improvements for larger knowledge bases

Access the Publication

You can access the full publication through IEEE Xplore: Question Answering System using Knowledge Graphs

Project Repository

The implementation and source code are available on GitHub: question-answering-knowledge-graph

This research represents a significant step forward in combining knowledge graphs with natural language processing for educational applications, demonstrating the potential for AI-driven personalized learning systems.