Most existing knowledge graph completion methods fail to model the fine-grained interactions among elements within triples, such as dependencies between entity attributes or contextual relationships involving predicates and entities. This limitation weakens their ability to infer implicit knowledge and hinders overall reasoning performance. To address this issue, we define a three-level classification of element interactions: Interactions between Elements at the Head Entity (IEH), Interactions between Elements at the Relationship (IER), and Interactions between Elements at the Tail Entity (IET), that systematically models the influence propagation patterns among knowledge graph triples at element level. Based on these interaction types, we propose a novel Knowledge Graph Completion Model Based on Fine-Grained Element Interactions (EI-KGC). Our model captures both global structural patterns and semantic dependencies within triples by combining GNN propagation with fine-grained interaction modeling. Experimental results show that the EI-KGC consistently outperforms traditional baseline models, demonstrating the high effectiveness of our proposed model.

Ambient environment information, including reflectors' location, dimension and reflectivity, is a key input to many millimeter-wave (mmWave) networking and sensing applications. It has found versatile applications in optimizing network coverage and robustness, enhancing mobile link performance, and enabling high-accuracy indoor localization and navigation. Recent approaches of deriving mmWave environment information require heavy infrastructure support or non-trivial human labor, and rely on costly software defined radios, which prevent their usage in practice. In this work, we design and implement mmRanger, a system can automatically sense environment without any infrastructure support. mmRanger equips a pair of low-cost off-the-shelf mmWave radios in a commodity robot, which constantly samples the ambient environment by exchanging a series of mmWave signals while it moves and rotates. mmRanger then re-engineers the time-domain signal series to derive the spatial-domain environment structure, through novel reflection path extraction and reflector mapping algorithms. Our experiments verify that mmRanger can accurately sense a given environment with minimal overhead, and the learned information can bring 1.6× and 2.1× performance gain, in terms of network coverage and mobile link throughput, respectively, over empirical approaches in mmWave networks.