feat: Implementation of evaluation metrics for Knowledge Graphs

[debrupf2946]

Implement Python Code for Knowledge Graph Evaluation Metric

Description

This issue invites contributors to implement a Python version of evaluation metrics proposed by other contributors. These metrics are essential for assessing the quality and performance of our knowledge graphs, and your implementation will directly contribute to enhancing our project's evaluation framework.If you would like to propose metrics, check #40

Objective

• Select a metric proposal submitted by another contributor (check the open issues with metric-proposal label).
• Develop a Python implementation of the selected metric.
• Ensure the implementation is efficient, well-documented, and tested against sample data.

Guidelines

1. Select a Metric: Review the issues tagged with metric-proposal to choose an evaluation metric that interests you and has not been implemented yet.
2. Understand the Metric: Carefully read the detailed review provided in the metric proposal issue, focusing on what it measures, how it measures, and any implementation notes.
3. Develop the Code: Implement the metric in Python, ensuring it aligns with the described methodology and requirements.
4. Document Your Work:
   • Include docstrings to explain your code clearly.
   • Provide comments where necessary to make the code understandable.
   • Jupyter notebook implementation of metric.
5. Submit a Pull Request (PR):
   • Reference the original metric proposal issue in your PR.
   • Include a brief description of your implementation, any challenges faced, and how you tested the metric.

Resources

• Review the metric proposal issue to understand the metric details.
• Use the provided research papers and any additional resources needed for accurate implementation.

Submission Checklist

• [ ] Select and claim a metric from the metric-proposal issues.
• [ ] Implement the metric in Python.
• [ ] Add comprehensive documentation
• [ ] Submit a PR referencing the metric proposal issue.

We appreciate your contributions and look forward to your implementations enhancing our knowledge graph evaluation capabilities!

[Kitsunnneee]

Hello @debrupf2946, Will it be okay to assign this issue to me, sir? I have previous experience in research in Hybrid-RAG systems and LLM fine tuning.

[debrupf2946]

@kmehant Can I assign @Kitsunnneee for this issue?