Existing research on temporal knowledge graph completion treats temporal information as supplementary, without simulating various features of facts from a temporal perspective. This work summarizes features of temporalized facts from both diachronic and synchronic perspectives: (1) Diachronicity. Facts often exhibit varying characteristics and trends across different temporal domains; (2) Synchronicity. In specific temporal contexts, various relations between entities influence each other, generating latent semantics. To track above issues, we design a quaternion-based model, TeDS, which divides timestamps into diachronic and synchronic timestamps to support dual temporal perception: (a) Two composite quaternions fusing time and relation information are generated by reorganizing synchronic timestamp and relation quaternions, and Hamilton operator achieves their interaction. (b) Each time point is sequentially mapped to an angle and converted to scalar component of a quaternion using trigonometric functions to build diachronic timestamps. We then rotate relation by using Hamilton operator between it and diachronic timestamp. In this way, TeDS achieves deep integration of relations and time while accommodating different perspectives. Empirically, TeDS significantly outperforms SOTA models on six benchmarks.

Effectively analyzing and mining large-scale heterogeneous information networks (HINs) by adopting network representation learning (NRL) approaches have received increasing attention. The abundant semantic and structural information contained in HINs not only facilitates network analysis and downstream tasks, but also poses special challenges to well capture that rich information. With the intention to preserve such rich yet potential information during HIN embedding, we first discuss the latent dependence existed in indirect neighbors, then study the different abilities of forward layer and backward layer of bidirectional recurrent neural network to remain semantic of HINs. And finally, we propose a novel representation learning model for HIN, namely RL4HIN. RL4HIN utilizes a skip-dependence strategy for enhancing the latent dependence between farther neighbors, and then develops a proposed weighted loss function in order to balance such difference between forward and backward layer. Extensive experiments, including node classification and visualization, have been conducted on two large- scale and real-world HINs. The experimental results show that RL4HIN significantly outperforms several state-of-the-art NRL approaches.