Knowledge Graph Completion (KGC) aims at inferring missing entities or relations by embedding them in a low-dimensional space. However, most existing KGC methods generally fail to handle the complex concepts hidden in triplets, so the learned embeddings of entities or relations may deviate from the true situation. In this paper, we propose a novel M ulti- c oncept R epresentation L earning method for KGC task (McRL), which mainly consists of a multi-concept representation module, a deep residual attention module and an interaction embedding module. Specifically, instead of the single-feature representation, the multi-concept representation module projects each entity or relation to multiple vectors to capture the complex conceptual information hidden in them. The deep residual attention module simultaneously explores the inter-connection and intra-connection between entities and relations to enhance the entity and relation embeddings corresponding to the current contextual situation. Moreover, the interaction embedding module further weakens the noise and ambiguity to obtain the optimal and robust embeddings. We conduct the link prediction experiment to evaluate the proposed method on several standard datasets, and experimental results show that the proposed method outperforms existing state-of-the-art KGC methods.

Dysphagia is one of the most common manifestations of stroke, which can affect as many as 50–81% of acute stroke patients. Despite the development of diverse treatment approaches, the precise mechanisms underlying therapeutic efficacy remain controversial. Earlier studies have revealed that the onset of dysphagia is associated with neurological damage. Neuroplasticity-based transcranial magnetic stimulation (TMS), a recently introduced technique, is widely used in the treatment of post-stroke dysphagia (PSD) by increasing changes in neurological pathways through synaptogenesis, reorganization, network strengthening, and inhibition. The main objective of this review is to discuss the effectiveness, mechanisms, potential limitations, and prospects of TMS for clinical application in PSD rehabilitation, with a view to provide a reference for future research and clinical practice.

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