Multi-level Fusion Knowledge Graph Completion Model

Knowledge graph completion aims to expand and enhance knowledge graphs by predicting missing triples. Multi-modal knowledge graph completion integrates entity ontology information such as entity descriptions, entity images, and entity attributes to obtain more accurate entity representations. Existing research projects different modalities into a unified space to obtain joint representations of entities, then combine knowledge graph structural information for predictions. However, existing methods have difficulty in capturing the complex interactions between entity background knowledge when fusing multi-modal information, which inevitably leads to information loss and insufficient feature extraction capabilities; overfitting and limited entity relation interactions restrict the performance of 2D convolution models, making it difficult to integrate knowledge graph structural information. Therefore, this paper uses a multi-level fusion knowledge graph completion model to address the above issues from two aspects: the fusion of entity multimodal information and the integration of knowledge graph structural information. To fully integrate entity multi-modal information, three different fusion methods are simultaneously used to comprehensively capture the interaction of entity background knowledge, along with decision learning, aiming to combine the complementary information provided by different multi-modal fusion methods to obtain rich and diverse entity representations. To fully integrate knowledge graph structural information, feature generalization is proposed to alleviate the overfitting issues of 2D convolution models, combined with feature reshaping to enhance interactions between entities and relations, thereby improving the contextual perception ability of entities and relations. Experiments on multiple public datasets demonstrate the superior performance of the proposed method.