{"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/multi-relational-poincare-graph-embeddings","title":"Multi-relational Poincaré Graph Embeddings","arxiv_id":"1905.09791","date":"2019-05-23","proceeding":"NeurIPS 2019 12","authors":["Ivana Balažević","Carl Allen","Timothy Hospedales"],"abstract":"Hyperbolic embeddings have recently gained attention in machine learning due to their ability to represent hierarchical data more accurately and succinctly than their Euclidean analogues. However, multi-relational knowledge graphs often exhibit multiple simultaneous hierarchies, which current hyperbolic models do not capture. To address this, we propose a model that embeds multi-relational graph data in the Poincar\\'e ball model of hyperbolic space. Our Multi-Relational Poincar\\'e model (MuRP) learns relation-specific parameters to transform entity embeddings by M\\\"obius matrix-vector multiplication and M\\\"obius addition. 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