Efficient Semantic Relationship and Representation Reconstruction Based on Knowledge Graph

Abstract

Knowledge graph is pivotal in natural language processing, having reached a mature stage of development. They are comprised of interconnected synsets, forming a structured graph network with significant potential for leveraging in graph neural networks (GNNs). However, the extensive number of nodes within knowledge graphs often entails substantial computational demands during application. This study employs the GNNs to facilitate data reconstruction and dimensionality reduction. Inno-vatively, we utilize the Graph AutoEncoder (GAE) to introduce a fusion decoding approach, thereby implementing a dual decoding mechanism aimed at reconstructing intricate relationships and semantic features. Additionally, we employ an efficient multi-graph training methodology to mitigate the impact of complex structures on the training process. Our research contributes a novel solution for compressing and reconstructing knowledge graph data, fostering deeper integration between natural language processing and graph networks, and yielding promising outcomes.