TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation

Abstract

In the last few years, there has been a surge of interest in learning representations of entities and relations in knowledge graph (KG). However, the recent availability of temporal knowledge graphs (TKGs) that contain time information for each fact created the need for reasoning over time in such TKGs. In this regard, we present a new approach of TKG embedding, TeRo, which defines the temporal evolution of entity embedding as a rotation from the initial time to the current time in the complex vector space. Specially, for facts involving time intervals, each relation is represented as a pair of dual complex embeddings to handle the beginning and the end of the relation, respectively. We show our proposed model overcomes the limitations of the existing KG embedding models and TKG embedding models and has the ability of learning and inferring various relation patterns over time. Experimental results on four different TKGs show that TeRo significantly outperforms existing state-of-the-art models for link prediction. In addition, we analyze the effect of time granularity on link prediction over TKGs, which as far as we know has not been investigated in previous literature.

Temporal Knowledge Graph Completion using a Linear Temporal Regularizer and Multivector Embeddings

“…Recent research on TKG completion shows that the inclusion of time information can improve the performances of KGE models on TKGs. TTransE (Leblay andChekol, 2018), HyTE (Dasgupta et al, 2018), ATiSE and TeRo (Xu et al, , 2020a propose scoring functions which incorporate time representations into a distancebased score function in different ways. Further-more, RTGE (Xu et al, 2020c) introduces the concept of temporal smoothness to optimize and learn the hyperplanes of adjacent time intervals jointly on the basis of HyTE.…”

“…We compare our models with the state-ofthe-art KGE model, ComplEx-N3 (Lacroix et al, 2018) and several existing TKGE approaches including TTransE (Leblay and Chekol, 2018), HyTE (Dasgupta et al, 2018), TA-TransE, TA-DistMult (García-Durán et al, 2018), ATiSE , TeRo (Xu et al, 2020a), DE-SimplE (Goel et al, 2020), TIME-PLEX(base) (Jain et al, 2020) and TCom-plEx (Lacroix et al, 2020). We do not use the complete TIME-PLEX model and the TNTCom-plEx model as baselines since the former incorporates additional temporal constraints for some specific relations and the latter is designed for modelling a KG where some facts involve time information and others do not.…”

Hyperbolic Temporal Knowledge Graph Embeddings with Relational and Time Curvatures

“…An advantage of ATISE is its ability to represent time uncertainty as the covariance of the Gaussian distributions. TERO (Xu et al, 2020b) combines ideas from TRANSE and ROTATE. It defines relations as translations and timestamps as rotations.…”

“…The final models for evaluation were selected upon the MRR metric on the validation set. We re-train ATISE and TERO using the same parameters as mentionned in Xu et al (2019) and Xu et al (2020b) but varying dimensions. 5…”

RTFE: A Recursive Temporal Fact Embedding Framework for Temporal Knowledge Graph Completion

“…In this subsection, we explore the effects of pre- ident, 2009). In recent years, some work (Jiang et al, 2016a;Esteban et al, 2016;Tresp et al, 2017;Trivedi et al, 2017;García-Durán et al, 2018;Jain et al, 2020;Ma et al, 2019;Xu et al, 2019;Jin et al, 2019;Wang and Li, 2019;Tang et al, 2020;Goel et al, 2019;Xu et al, 2020;Jain et al, 2020;Lacroix et al, 2020;) began to use the time information to improve the KG completion or directly complete the TKG. Based on the fact or event they dealt with, we state representative TKGE methods as follows.…”