Multi-Scale Graph Neural Networks for Semantic Understanding

Abstract

This paper proposes a novel approach to semantic understanding utilizing multi-scale graph neural networks (MS-GNNs). Traditional semantic understanding models often operate within a single scale of representation, limiting their ability to capture nuanced relationships and contextual information. Our method addresses this limitation by constructing a MS-GNN architecture where each scale corresponds to a distinct semantic representation strategy. We employ graph convolutional operations to effectively fuse information from these diverse scales, ultimately enhancing the accuracy and robustness of the semantic understanding process. The core claim is that a multi-scale approach significantly improves semantic understanding by simultaneously capturing information at various levels of detail. This work introduces a new framework for representing and reasoning about complex semantic relationships, offering a promising direction for future research in areas such as knowledge graph completion, relation extraction, and scene understanding. The proposed MS-GNN model aims to learn richer and more comprehensive representations of semantic data, leading to improved performance across a range of downstream tasks. The key mechanism involves the iterative refinement of semantic representations through graph convolution across multiple scales.