Relational machine learning studies methods for the statistical analysis of relational, or
graph-structured, data. In this paper, we provide a review of how such statistical models can …

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 …

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 …

The natural habitat of most Bayesian methods is data represented by exchangeable
sequences of observations, for which de Finetti's theorem provides the theoretical …

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 …