Aligning Internal Regularity and External Influence of Multi-granularity for Temporal Knowledge Graph Embedding

Tingyi Zhang, Zhixu Li, Jiaan Wang, Jianfeng Qu, Lin Yuan, An Liu, Lei Zhao, Zhigang Chen

Abstract: Representation learning for the Temporal Knowledge Graphs (TKGs) is an emerging topic in the knowledge reasoning community. Existing methods consider the internal and external influence at either element level or fact level. However, the multi-granularity information is essential for TKG modeling and the connection in between is also under-explored. In this paper, we propose the method that Aligning-internal Regularity and external Influence of Multi-granularity for Temporal knowledge graph Embedding (ARIM-TE). In particular, to prepare considerate source information for alignment, ARIM-TE first models element-level information via the addition between internal regularity and the external influence. Based on the element-level information, the merge gate is introduced to model the fact-level information by combining their internal regularity including the local and global influence with external random perturbation. Finally, according to the above obtained multi-granular information of rich features, ARIM-TE conducts alignment for them in both structure and semantics. Experimental results show that ARIM-TE outperforms current state-of-the-art KGE models on several TKG link prediction benchmarks.