Research on link prediction in knowledge graphs has mainly focused on static multi-relational data. In this work we consider temporal knowledge graphs where relations between entities may only hold for a time interval or a specific point in time. In line with previous work on static knowledge graphs, we propose to address this problem by learning latent entity and relation type representations. To incorporate temporal information, we utilize recurrent neural networks to learn time-aware representations of relation types which can be used in conjunction with existing latent factorization methods. The proposed approach is shown to be robust to common challenges in real-world KGs: the sparsity and heterogeneity of temporal expressions. Experiments show the benefits of our approach on four temporal KGs. The data sets are available under a permissive BSD-3 license 1.
There is also prior work on incorporating temporal information in knowledge graph completion methods. Jiang2016TowardsTK ( Jiang2016TowardsTK ) capture the temporal ordering that exists between some relation types as well as additional common-sense constraints to generate more accurate link predictions. Esteban2016 ( Esteban2016 ) introduce a prediction model for link prediction that assumes that changes to a KG are introduced by incoming events. These events are modeled as a separate event graph and used to predict the existence of links in the future. knowevolve ( knowevolve ) model the occurrence of a fact as a point process whose intensity function is influenced by the score assigned to the fact by an embedding function. Leblay:2018 ( Leblay:2018 ) develop scoring functions that incorporate time representations into a TransE-type scoring function. Prior work has also incorporated numerical but non-temporal entity information for knowledge base completion @cite_6 .
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We present KBLRN, a framework for end-to-end learning of knowledge base representations from latent, relational, and numerical features. KBLRN integrates feature types with a novel combination of neural representation learning and probabilistic product of experts models. To the best of our knowledge, KBLRN is the first approach that learns representations of knowledge bases by integrating latent, relational, and numerical features. We show that instances of KBLRN outperform existing methods on a range of knowledge base completion tasks. We contribute a novel data sets enriching commonly used knowledge base completion benchmarks with numerical features. The data sets are available under a permissive BSD-3 license. We also investigate the impact numerical features have on the KB completion performance of KBLRN.