Pykg2vec: A Python Library for Knowledge Graph Embedding
arXiv:1906.04239
Abstract
Pykg2vec is an open-source Python library for learning the representations of the entities and relations in knowledge graphs. Pykg2vec's flexible and modular software architecture currently implements 16 state-of-the-art knowledge graph embedding algorithms, and is designed to easily incorporate new algorithms. The goal of pykg2vec is to provide a practical and educational platform to accelerate research in knowledge graph representation learning. Pykg2vec is built on top of TensorFlow and Python's multiprocessing framework and provides modules for batch generation, Bayesian hyperparameter optimization, mean rank evaluation, embedding, and result visualization. Pykg2vec is released under the MIT License and is also available in the Python Package Index (PyPI). The source code of pykg2vec is available at https://github.com/Sujit-O/pykg2vec.
5 pages, 5 figures, few code snippets
Cited by in corpus (5)
- PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings
- Exploiting Non-Taxonomic Relations for Measuring Semantic Similarity and Relatedness in WordNet
- TorchKGE: Knowledge Graph Embedding in Python and PyTorch
- Benchmarking neural embeddings for link prediction in knowledge graphs under semantic and structural changes
- On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods