Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs. In this work, we propose …
Recent years have witnessed a proliferation of large-scale knowledge bases, including Wikipedia, Freebase, YAGO, Microsoft's Satori, and Google's Knowledge Graph. To increase …
A Three-Way Model for Collective Learning on Multi-Relational Data - 28th International Conference on Machine Learning Page 1 Poster at Session P2 A Three-Way Model for …
An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of …
S Guo, Q Wang, L Wang, B Wang… - Proceedings of the 2016 …, 2016 - aclanthology.org
Embedding knowledge graphs into continuous vector spaces has recently attracted increasing interest. Most existing methods perform the embedding task using only fact …
Observations consisting of measurements on relationships for pairs of objects arise in many settings, such as protein interaction and gene regulatory networks, collections of author …
P Orbanz, DM Roy - IEEE transactions on pattern analysis and …, 2014 - ieeexplore.ieee.org
The natural habitat of most Bayesian methods is data represented by exchangeable sequences of observations, for which de Finetti's theorem provides the theoretical …
K Miller, M Jordan, T Griffiths - Advances in neural …, 2009 - proceedings.neurips.cc
As the availability and importance of relational data--such as the friendships summarized on a social networking website--increases, it becomes increasingly important to have good …
The emergence of various disruptive technologies such as big data, Internet of Things, and artificial intelligence have instigated our society to generate enormous volumes of data. The …