Welcome to the Graph Embedding Toolkit repository, a comprehensive resource for various code snippets essential to our research project. This repository covers critical phases of our research methodology, including the construction of a knowledge graph, embedding generation, model training, and predictive analysis. For detailed documentation and access to the code snippets, please visit our GitHub Repository.
In this section, you will find code segments that facilitate the construction and structuring of our knowledge graph. These snippets take raw data sources and transform them into a structured knowledge graph, a fundamental component of our research.
Our repository includes scripts for generating embeddings from the constructed knowledge graph. These embeddings serve as representations of entities within the graph and play a crucial role in our research.
This section contains code components for training machine learning models. These models are designed for various tasks, such as classification, clustering, or other relevant objectives. They leverage the embeddings generated earlier to make predictions and extract valuable insights from the data.
The code snippets in this part of the repository are dedicated to conducting predictive analysis. You can use the trained machine learning models to make predictions, assess model performance, and draw conclusions aligned with our research objectives.
Thank you for visiting our Graph Embedding Toolkit repository. We hope you find these code snippets valuable for your own research and projects.