To handle new entities not present in the training data, the proposed method can be extended by incorporating techniques for entity alignment or entity linking. By leveraging external knowledge sources or entity embeddings, the model can map new entities to the existing entity space. This alignment process can help in integrating the new entities into the tensor decomposition framework, allowing the model to make predictions even for unseen entities. Additionally, techniques such as zero-shot learning or few-shot learning can be employed to generalize the model's knowledge to new entities based on their similarities to existing entities in the knowledge graph.