TSMixerE: Entity Context-Aware Method for Static Knowledge Graph Completion
Jianzhong Chen, Yunsheng Xu, Zirui Guo, Tianmin Liu, Ying Pan*
Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, 530001, China
* Corresponding Author: Ying Pan. Email: 
Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.071777
Received 12 August 2025; Accepted 22 December 2025; Published online 19 January 2026
Abstract
The rapid development of information technology and accelerated digitalization have led to an explosive growth of data across various fields. As a key technology for knowledge representation and sharing, knowledge graphs play a crucial role by constructing structured networks of relationships among entities. However, data sparsity and numerous unexplored implicit relations result in the widespread incompleteness of knowledge graphs. In static knowledge graph completion, most existing methods rely on linear operations or simple interaction mechanisms for triple encoding, making it difficult to fully capture the deep semantic associations between entities and relations. Moreover, many methods focus only on the local information of individual triples, ignoring the rich semantic dependencies embedded in the neighboring nodes of entities within the graph structure, which leads to incomplete embedding representations. To address these challenges, we propose Two-Stage Mixer Embedding (TSMixerE), a static knowledge graph completion method based on entity context. In the unit semantic extraction stage, TSMixerE leverages multi-scale circular convolution to capture local features at multiple granularities, enhancing the flexibility and robustness of feature interactions. A channel attention mechanism amplifies key channel responses to suppress noise and irrelevant information, thereby improving the discriminative power and semantic depth of feature representations. For contextual information fusion, a multi-layer self-attention mechanism enables deep interactions among contextual cues, effectively integrating local details with global context. Simultaneously, type embeddings clarify the semantic identities and roles of each component, enhancing the model’s sensitivity and fusion capabilities for diverse information sources. Furthermore, TSMixerE constructs contextual unit sequences for entities, fully exploring neighborhood information within the graph structure to model complex semantic dependencies, thus improving the completeness and generalization of embedding representations.
Keywords
Knowledge graph; knowledge graph complementation; convolutional neural network; feature interaction; context
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