目前，将知识表示学习应用于知识图谱补全任务以预测知识图谱中缺失的实体或关系取得了不错的成效，但多数传统的基于表示学习的补全方法仅考虑了经过数据构建后的三元组中的结构特征，这使得原始数据中丰富的文本、图像等多种模态信息未被有效利用。针对此问题，提出将三元组的能量函数定义为利用关系的文本特征和实体的多模态特征进行表示的子能量函数的总和，以充分发掘三元组中的语义信息。此外，针对不同的能量函数设计了一种多模态负采样策略，用以对齐实体的不同特征，提高知识表示的质量，提升对知识图谱补全的性能。在公开数据集DB15K和FB15K-237上进行链接预测实验，结果表明所提出模型具有较好的知识图谱补全能力。

Currently, knowledge representation learning has been effectively utilized in knowledge graph completion tasks to predict missing entities or relationships within knowledge graphs, achieving considerable success. However, the structural features of triples after data construction are the only considerations for most traditional completion methods based on representation learning, the results in the rich textual, image, and other multimodal information in the original data not being effectively utilized. To address this problem, it is proposed that the energy function of triples should be defined as the sum of sub-energy functions, which are characterized by the textual features of relationships and the multimodal features of entities, to fully exploit the semantic information in the triple. In addition, a multimodal negative sampling strategy has been devised for diverse energy functions, aiming to align the disparate features of entities, enhance the quality of knowledge representation, and boost the performance of knowledge graph completion. Link prediction experiments were conducted on publicly available datasets DB15K and FB15K-237. The results show that the proposed model exhibits remarkable capabilities in knowledge graph completion.