Wasserstein Adversarial Learning based Temporal Knowledge Graph Embedding

TL;DR

This paper introduces a novel adversarial learning framework for temporal knowledge graph embedding that enhances the quality of negative samples and improves model performance using Wasserstein distance and Gumbel-Softmax techniques.

Contribution

It proposes a new adversarial learning approach with a generator and discriminator for TKGE, incorporating Wasserstein distance and Gumbel-Softmax to address vanishing gradients and improve embedding quality.

Findings

Significant performance improvements over benchmark models

Effective generation of high-quality negative samples

Demonstrated applicability on multiple temporal datasets

Abstract

Research on knowledge graph embedding (KGE) has emerged as an active field in which most existing KGE approaches mainly focus on static structural data and ignore the influence of temporal variation involved in time-aware triples. In order to deal with this issue, several temporal knowledge graph embedding (TKGE) approaches have been proposed to integrate temporal and structural information in recent years. However, these methods only employ a uniformly random sampling to construct negative facts. As a consequence, the corrupted samples are often too simplistic for training an effective model. In this paper, we propose a new temporal knowledge graph embedding framework by introducing adversarial learning to further refine the performance of traditional TKGE models. In our framework, a generator is utilized to construct high-quality plausible quadruples and a discriminator learns to obtain the embeddings of entities and relations based on both positive and negative samples. Meanwhile, we also apply a Gumbel-Softmax relaxation and the Wasserstein distance to prevent vanishing gradient problems on discrete data; an inherent flaw in traditional generative adversarial networks. Through comprehensive experimentation on temporal datasets, the results indicate that our proposed framework can attain significant improvements based on benchmark models and also demonstrate the effectiveness and applicability of our framework.