The TransR model solves the problem that TransE and TransH models are not sufficient for modeling in public spaces, and is considered a highly potential knowledge representation model. However, TransR still adopts the translation principles based on the TransE model, and the constraints are too strict, which makes the model’s ability to distinguish between very similar entities low. Therefore, we propose a representation learning model TransR* based on flexible translation and relational matrix projection. Firstly, we separate entities and relationships in different vector spaces; secondly, we combine our flexible translation strategy to make translation strategies more flexible. During model training, the quality of generating negative triples is improved by replacing semantically similar entities, and the prior probability of the relationship is used to distinguish the relationship of similar coding. Finally, we conducted link prediction experiments on the public data sets FB15K and WN18, and conducted triple classification experiments on the WN11, FB13, and FB15K data sets to analyze and verify the effectiveness of the proposed model. The evaluation results show that our method has a better improvement effect than TransR on Mean Rank, Hits@10 and ACC indicators.
Comprehensive SummaryWith high water content, excellent biocompatibility and lubricating properties, and a microstructure similar to that of the extracellular matrix, hydrogel is becoming one of the most promising materials as a substitute for articular cartilage. However, it is a challenge for hydrogel materials to simultaneously satisfy high loading and low friction. Most hydrogels are brittle, with fracture energies of around 10 J·m−2, as compared with ∼1000 J·m−2 for cartilage. A great deal of effort has been devoted to the synthesis of hydrogels with improved mechanical properties, such as increasing the compactness of the polymer network, introducing dynamic non‐covalent bonds, and increasing the hydrophobicity of the polymer, all at the expense of the lubricating properties of the hydrogel. Herein, we develop a hydrogel material with anisotropic tubular structures where the compactness gradually decreases and eventually disappears from the surface to the subsurface, achieving a balance between lubrication and load‐bearing. The porous layer with hydrophilic carboxyl groups on the surface exhibits extremely low friction (coefficient of friction (COF) ∼0.003, 1 N; COF ∼0.08, 20 N) against the hard steel ball, while the bottom layer acts as an excellent load‐bearing function. What is more, the gradual transition of the tubular structures between the surface and the subsurface ensures the uniform distribution of friction stress between a lubricating and bearing layers, which endows the material with long‐lasting and smooth friction properties. The extraordinary lubricious performance of the hydrogels with anisotropic tubular structure has potential applications in tissue engineering and medical devices.
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