Best Python Packages (Tools) For Knowledge Graphs

Knowledge Graphs are advanced data structures that facilitate complex queries across multiple data silos by organizing data into tables and graphs, allowing for high connectivity and adaptability to new information. Various Python packages are available to work with knowledge graphs, each offering unique features and benefits. Pykg2vec focuses on knowledge graph embedding with a framework built initially on TensorFlow and later adapted to PyTorch, offering Bayesian hyperparameter optimization and interactive visualizations. PyKEEN provides tools for building and evaluating knowledge graph embeddings with features like automatic memory optimization and community-driven tools. AmpliGraph democratizes graph representation learning, enabling users to generate and utilize knowledge graph embeddings for tasks like link prediction. LibKGE aims to enhance research in KGE models by providing tools for training, hyperparameter optimization, and logging, supporting a wide range of models and configurations. GraphVite combines a C++ core library with a Python interface to enable high-speed processing of large-scale graphs and supports dynamic data types and high-dimensional data visualization.