Link Prediction Based on Data Augmentation and Metric Learning Knowledge Graph Embedding

Abstract

A knowledge graph is a repository that represents a vast amount of information in the form of triplets. In the training process of completing the knowledge graph, the knowledge graph only contains positive examples, which makes reliable link prediction difficult, especially in the setting of complex relations. At the same time, current techniques that rely on distance models encapsulate entities within Euclidean space, limiting their ability to depict nuanced relationships and failing to capture their semantic importance. This research offers a unique strategy based on Gibbs sampling and connection embedding to improve the model’s competency in handling link prediction within complex relationships. Gibbs sampling is initially used to obtain high-quality negative samples. Following that, the triplet entities are mapped onto a hyperplane defined by the connection. This procedure produces complicated relationship embeddings loaded with semantic information. Through metric learning, this process produces complex relationship embeddings imbued with semantic meaning. Finally, the method’s effectiveness is demonstrated on three link prediction benchmark datasets FB15k-237, WN11RR and FB15k.

Keywords

• knowledge graph embedding
• metric learning
• link prediction
• negative sampling
• semantic extraction
• relation fusion