{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pykeen-1-0-a-python-library-for-training-and","title":"PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings","arxiv_id":"2007.14175","date":"2020-07-28","proceeding":null,"authors":["Mehdi Ali","Max Berrendorf","Charles Tapley Hoyt","Laurent Vermue","Sahand Sharifzadeh","Volker Tresp","Jens Lehmann"],"abstract":"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. 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