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• W4315465008 abstract "Knowledge graphs are involved in more and more applications to further improve intelligence. Owing to the inherent incompleteness of knowledge graphs resulted from data updating and missing, a number of knowledge graph completion models are proposed in succession. To obtain better performance, many methods are of high complexity, making it time-consuming for training and inference. This paper proposes a simple but effective model using only shallow neural networks, which combines enhanced feature interaction and multi-subspace information integration. In the enhanced feature interaction module, entity and relation embeddings are almost peer-to-peer interacted via multi-channel 2D convolution. In the multi-subspace information integration module, entity and relation embeddings are projected to multiple subspaces to extract multi-view information to further boost performance. Extensive experiments on widely used datasets show that the proposed model outperforms a series of strong baselines. And ablation studies demonstrate the effectiveness of each submodule in the model." @default.
• W4315465008 created "2023-01-11" @default.
• W4315465008 creator A5029065475 @default.
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• W4315465008 date "2023-02-22" @default.
• W4315465008 modified "2023-10-16" @default.
• W4315465008 title "ConvMS: Improving Convolutional Knowledge Graph Embeddings via Integrating Information of Multiple Subspaces" @default.
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• W4315465008 doi "https://doi.org/10.1142/s2737480723500012" @default.
• W4315465008 hasPublicationYear "2023" @default.
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