A Knowledge Graph Completion Method Based on Gated Adaptive Fusion and Conditional Generative Adversarial Networks

The multimodal knowledge graph completion (MMKGC) task aims to acquire accurate entity representations by learning different modal information about entities, which can be used to predict missing entities or relations in knowledge graphs (KGs). The lack of multimodal information accumulation and the noise in the various modal information collected leads to different modal information contributing differently to knowledge graph completion (KGC). Therefore, it becomes crucial to effectively fusion and fully utilize the multimodal information. To solve the above problems, we propose a knowledge graph completion method based on gated adaptive fusion and conditional generative adversarial networks (GAF-CGAN). GAF-CGAN is based on a gating mechanism for deeply extracting critical information in different modal features. After this, the model fuses the features by adapting the weights between different modal features. Ultimately, we utilize the relations in the triples as conditions to guide the generator in generating fake samples with specific meanings for adversarial training, enhancing the model’s robustness. We conduct link prediction experiments on two publicly available datasets, DB15K and FB15K-237, and the results show that our method significantly outperforms existing benchmark methods and effectively improves the model’s capacity for KGC.