Temporal Knowledge Graph (TKG) representation learning embeds entities and relations into a low-dimensional space while preserving relational structures across time steps. Existing methods often neglect the critical role of timestamps in capturing evolving relational patterns. To bridge this gap, we propose MTT-TKG, a novel framework integrating three synergistic modules: (1) a Multi-Time Gate module modeling Knowledge Graph (KG) evolution across historical timestamps via multilayer gating; (2) a Time-Aware module capturing timestampspecific relational characteristics; (3) a Time-Guided module handling cross-graph temporal dependencies. An embeddingtime decoder completes the representation learning. Experiments on three real-world datasets demonstrate MTT-TKG’s superior performance in capturing temporal dynamics and relational structures.

Knowledge graph completion (KGC) tasks aim to infer missing facts in a knowledge graph (KG) for many knowledgeintensive applications. However, existing embedding-based KGC approaches primarily rely on factual triples, potentially leading to outcomes inconsistent with common sense. Besides, generating explicit common sense is often impractical or costly for a KG. To address these challenges, we propose a pluggable common sense-enhanced KGC framework that incorporates both fact and common sense for KGC. This framework is adaptable to different KGs based on their entity concept richness and has the capability to automatically generate explicit or implicit common sense from factual triples. Furthermore, we introduce common senseguided negative sampling and a coarse-to-fine inference approach for KGs with rich entity concepts. For KGs without concepts, we propose a dual scoring scheme involving a relation-aware concept embedding mechanism. Importantly, our approach can be integrated as a pluggable module for many knowledge graph embedding (KGE) models, facilitating joint common sense and fact-driven training and inference. The experiments illustrate that our framework exhibits good scalability and outperforms existing models across various KGC tasks.

A two‐stream remote sensing image fusion network (RCAMTFNet) based on the residual channel attention mechanism is proposed by introducing the residual channel attention mechanism (RCAM) in this paper. In the RCAMTFNet, the spatial features of PAN and the spectral features of MS are extracted, respectively, by a two‐channel feature extraction layer. Multiresidual connections allow the network to adapt to a deeper network structure without the degradation. The residual channel attention mechanism is introduced to learn the interdependence between channels, and then the correlation features among channels are adapted on the basis of the dependency. In this way, image spatial information and spectral information are extracted exclusively. What is more, pansharpening images are reconstructed across the board. Experiments are conducted on two satellite datasets, GaoFen‐2 and WorldView‐2. The experimental results show that the proposed algorithm is superior to the algorithms to some existing literature in the comparison of the values of reference evaluation indicators and nonreference evaluation indicators.