Dynamic Semantic Embedding Network (DSE-Net)

Abstract

This paper introduces the Dynamic Semantic Embedding Network (DSE-Net), a novel approach to understanding evolving data streams. The core claim is that by integrating semantic embeddings with dynamic graph neural networks, we can achieve continuous, context-aware understanding, overcoming the limitations of static embedding models. DSE-Net employs a multi-layered architecture: a Transformer encoder for initial semantic embedding generation, a dynamic graph neural network (GNN) to model relationships within the data stream, and a reinforcement learning (RL) module to optimize the GNN's structure and parameters adaptively. Crucially, the embedding itself is updated based on the GNN's output, creating an evolving semantic representation. This approach addresses the shortcomings of existing methods, which either rely on static embeddings or static GNNs, by providing a dynamic and learning system capable of adapting to changing semantic relationships. The key innovation lies in the synergistic combination of these techniques, leading to a more robust and nuanced understanding of dynamic data. ---