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Knowledge graph embedding methods learn embeddings of entities and relations in a low dimensional space which can be used for various downstream machine learning tasks such as link prediction and entity matching. Various graph convolutional network methods have been proposed which use different types of information to learn the features of entities...
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... this end, we follow [15] and apply a shared linear transformation on triplets (ℎ, í µí±, í µí±¡), parameterized by a weight matrix W. Then, we perform self-attention with respect to a shared relation between entities to compute attention coefficients: í µí± : R í µí± × R í µí± × R í µí± → R. The attention mechanism is shown in Figure 2. The attention coefficient of a triplet (ℎ, í µí±, í µí±¡) is computed as: ...
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... Rather than treating knowledge as auxiliary features, this conceptual module aims to embed physically meaningful relationships, such as propagation principles and environmental semantics, into the model's latent space to guide learning in a physics-consistent manner. Potential implementation approaches include relation-aware attention [45,46], symbolic embeddings [47], or multilevel alignment mechanisms [48] that map knowledge representations to the internal layers of the model. By incorporating the WEKP in this manner, the model is better constrained during training, mitigating issues common in purely data-driven approaches, such as limited generalization and poor adaptability in unfamiliar environments. ...
With the growing demand for wireless communication, the sixth-generation (6G) wireless network will be more complex. The digital twin channel (DTC) is envisioned as a promising enabler for 6G, as it can create an online replica of the physical channel characteristics in the digital world, thereby supporting precise and adaptive communication decisions for 6G. In this article, we systematically review and summarize the existing efforts in realizing the DTC, providing a comprehensive analysis of ray tracing (RT), artificial intelligence (AI), and large model approaches. Based on this analysis, we further explore the potential of integrating large models with RT methods. By leveraging the strong generalization, multi-task processing capabilities, and multi-modal fusion capabilities of large models while incorporating physical priors from RT as expert knowledge to guide their training, there is a strong possibility of fulfilling the fast online inference and precise mapping requirements of the DTC. Therefore, we propose a novel DeepRT-enabled DTC (DRT-DTC) framework, which combines physical laws with large models like DeepSeek, offering a new vision for realizing the DTC. Two case studies are presented to demonstrate the possibility of this approach, which validate the effectiveness of physical law-based AI methods and large models in generating the DTC.
