Presentation
AM-DGCNN: Leveraging Graph Attention Networks and Edge Attributes for Link Classification in Knowledge Graphs
DescriptionGraph-based representations are increasingly popular for storing and managing information through knowledge graphs, which capture entities and their relationships. However, these knowledge graphs often suffer from incomplete link information. To address this issue, link classification methods can be used to predict and verify missing connections. Recently, supervised heuristic learning methods have improved link classification accuracy. Specifically, the SEAL framework, as a state-of-the-art supervised heuristic learning tool, excels in learning associativity patterns by analyzing local enclosing subgraphs to classify links. However, DGCNN, a graph neural network model in this framework, lacks the capability to process edge attributes, leading to poor classification accuracy in knowledge graphs. Hence, this paper proposes an Augmented Model of the DGCNN (AM-DGCNN) by replacing GCNs with GATs to better incorporate link information. With extensive experiments, we demonstrate that our AM-DGCNN in the SEAL framework can achieve up to 98% accuracy for classifying links in knowledge graphs.
