论文标题
Pykeen 1.0：用于培训和评估知识图嵌入的Python库
PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings
论文作者
论文摘要
最近，知识图嵌入（KGES）受到了极大的关注，并且已经开发了一些软件库来培训和评估KGE。尽管每个人都满足特定需求，但我们在社区努力中重新设计并重新设计了Pykeen，这是第一批KGE图书馆之一。 Pykeen 1.0使用户能够基于广泛的交互模型，训练方法，损失功能，构成知识图嵌入模型（KGEM），并允许对逆关系的明确建模。此外，已经实现了自动内存优化，以最佳利用所提供的硬件，并通过集成Optuna广泛的超参数优化（HPO）功能。
Recently, knowledge graph embeddings (KGEs) received significant attention, and several software libraries have been developed for training and evaluating KGEs. While each of them addresses specific needs, we re-designed and re-implemented PyKEEN, one of the first KGE libraries, in a community effort. PyKEEN 1.0 enables users to compose knowledge graph embedding models (KGEMs) based on a wide range of interaction models, training approaches, loss functions, and permits the explicit modeling of inverse relations. Besides, an automatic memory optimization has been realized in order to exploit the provided hardware optimally, and through the integration of Optuna extensive hyper-parameter optimization (HPO) functionalities are provided.
