An edge enhancement graph neural network model with node discrimination for knowledge graph representation learning

Abstract

Abstract The vectorized representation of a knowledge graph is essential for effectively utilizing its implicit knowledge. Graph neural networks (GNNs) are particularly adept at learning graph representations due to their ability to handle graph topologies. However, GNN-based approaches face two main challenges: first, they fail to differentiate between the types of adjacent nodes during the information aggregation process; second, the edge representations lack relational semantic information and fail to capture the characteristics of adjacent nodes. Conventional methods typically treat source and destination nodes as identical, ignoring the distinct information that arises from different node types. This results in a failure to accurately capture the various semantic features, leading to feature redundancy. Additionally, many existing methods derive edge representations through random initialization or linear transformations, which do not adequately reflect relational semantics and adjacent node information, resulting in ineffective edge representations.To address these limitations, we propose the Edge Enhancement GNN model with Node Discrimination (NDEE-GNN). This model establishes node discrimination information aggregation mechanisms for source and destination nodes, allowing for a deeper investigation into the impact of various adjacent node types. It also employs a specially designed information aggregation mechanism for each edge, incorporating relation and adjacent node features. Experimental results across multiple real-world datasets demonstrate that by discriminating node types and enhancing edge representations, NDEE-GNN can accurately capture and represent complex associations between entities and relations, significantly improving link prediction performance and outpacing other baseline models.