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Papers

PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings

2020-07-28 · Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt, Laurent Vermue, Sahand Sharifzadeh, Volker Tresp, Jens Lehmann

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.

📄 PDF Abstract BibTeX arXiv:2007.14175

Code (2)

pykeen/pykeen 공식 구현 pytorch
SmartDataAnalytics/PyKEEN pytorch

Tasks

Graph EmbeddingKnowledge Graph EmbeddingKnowledge Graph EmbeddingsLink Prediction

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