  Most algorithms for representation learning and link prediction in relational
data have been designed for static data. However, the data they are applied to
usually evolves with time, such as friend graphs in social networks or user
interactions with items in recommender systems. This is also the case for
knowledge bases, which contain facts such as (US, has president, B. Obama,
[2009-2017]) that are valid only at certain points in time. For the problem of
link prediction under temporal constraints, i.e., answering queries such as
(US, has president, ?, 2012), we propose a solution inspired by the canonical
decomposition of tensors of order 4. We introduce new regularization schemes
and present an extension of ComplEx (Trouillon et al., 2016) that achieves
state-of-the-art performance. Additionally, we propose a new dataset for
knowledge base completion constructed from Wikidata, larger than previous
benchmarks by an order of magnitude, as a new reference for evaluating temporal
and non-temporal link prediction methods.
