MASZSL: A Multi-Block Attention-Based Description Generative Adversarial Network for Knowledge Graph Zero-Shot Relational Learning

作者：Mei Yu, Pengtao Fan, Mankun Zhao, Wenbin Zhang, Yue Zhao, Ming Yang, Jian Yu · 发表于：IEEE International Joint Conference on Neural Network · 年份：2023 · DOI：10.1109/IJCNN54540.2023.10191061 · 被引用次数：2 · 研究领域：Computer Science

In the real world, the Knowledge Graph(KG) is dynamic and new entities are added at any time. Therefore, open-world Knowledge Graph Completion(KGC) was proposed to approach new-added entities, but previous approaches often introduced too much noise when introducing external text resources for new entities. To alleviate this problem, knowledge graph zero-shot relational learning (KGZSL) has recently attracted more attention. Generative Adversarial Networks (GANs) are frequently used in KGZSL to connect existing relation descriptions to the domain of knowledge graphs. However, these methods ignore the impact of existing entities on embeddings for unidentified relational representations and in-stead concentrate on examining the connection between relational textual texts and knowledge network structures. In this work, we propose a multi-block attention framework using relation Description Generative Adversarial Networks (desGAN) jointing KG and text representation and address model collapse and training stability problems in previous studies. The core idea of our method is to obtain the background knowledge graph information and the relation representation through and multi-block attention layer and the desGAN, then a connection between the structured KG semantic space and the unstructured text semantic space of the new entity is established, forcing the entity pair to be closer to their real relation. Experimental results on the knowledge graph zeroshot relational learning data...