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Introduction:

The field of graph neural networks has emerged as a powerful tool for analyzing and processing complex data with graph structures. In recent years, there has been a growing interest in using graph neural networks for knowledge graph completion, a task that aims to predict missing links in a knowledge graph. Knowledge graphs are structured data representations that encode relationships between entities in a domain, and are commonly used in various applications such as recommendation systems, search engines, and semantic web.

This thesis focuses on the application of graph neural networks for knowledge graph completion, with the goal of improving the accuracy and efficiency of link prediction tasks. The study will explore various graph neural network architectures and techniques for knowledge graph completion, and evaluate their performance on benchmark datasets.

Table of Contents:

Chapter 1: Introduction

1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review

2.1 Overview of Graph Neural Networks
2.2 Knowledge Graphs
2.3 Knowledge Graph Completion
2.4 Link Prediction in Knowledge Graphs
2.5 Graph Neural Network Architectures
2.6 Graph Embedding Techniques
2.7 Evaluation Metrics for Link Prediction
2.8 Related Work in Knowledge Graph Completion
2.9 Challenges and Opportunities in Graph Neural Networks for Knowledge Graph Completion
2.10 Summary of Literature Review

Chapter 3: Research Methodology

3.1 Data Collection and Preprocessing
3.2 Graph Neural Network Model Selection
3.3 Hyperparameter Tuning
3.4 Training and Evaluation
3.5 Performance Metrics
3.6 Experiment Design
3.7 Comparison with Baseline Methods
3.8 Ethical Considerations in Research
3.9 Summary of Research Methodology

Chapter 4: Discussion of Findings

4.1 Performance Evaluation of Graph Neural Networks
4.2 Analysis of Model Interpretability
4.3 Comparison with Baseline Methods
4.4 Impact of Hyperparameters on Model Performance
4.5 Insights from Experimental Results
4.6 Limitations and Future Directions
4.7 Practical Implications of Research Findings
4.8 Contribution to the Field
4.9 Summary of Discussion

Chapter 5: Conclusion and Summary

5.1 Summary of Findings
5.2 Conclusion
5.3 Contributions of the Study
5.4 Future Research Directions
5.5 Final Remarks

Thesis Overview:

Graph neural networks have gained significant attention in recent years due to their effectiveness in analyzing and processing graph-structured data. In the context of knowledge graph completion, the task of predicting missing links in knowledge graphs is a crucial problem with various real-world applications. This thesis aims to explore the application of graph neural networks for knowledge graph completion and evaluate their performance on benchmark datasets.

The literature review will provide a comprehensive overview of graph neural networks, knowledge graphs, knowledge graph completion, and related work in the field. The research methodology section will outline the data collection and preprocessing steps, model selection, training and evaluation processes, and performance metrics used for the study. The discussion of findings will include an analysis of the performance of graph neural networks, comparison with baseline methods, and insights from experimental results.

Overall, this thesis will contribute to the existing body of knowledge on graph neural networks for knowledge graph completion and provide valuable insights for future research in this area. The findings of this study will have practical implications for improving the accuracy and efficiency of link prediction tasks in knowledge graphs.

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