One potential limitation of the SpherE model is its scalability to very large knowledge graphs with millions of entities and relationships. As the size of the knowledge graph increases, the computational complexity of the sphere-based modeling in SpherE may become prohibitive, leading to longer training times and higher resource requirements. To address this limitation, future research could explore optimization techniques and parallel processing methods to enhance the efficiency of SpherE on large-scale knowledge graphs. Additionally, incorporating techniques like mini-batch training and distributed computing could help mitigate the scalability issues and improve the model's performance on massive knowledge graphs.
Another limitation of SpherE is its reliance on Euclidean space for modeling entity spheres. This assumption may not always hold true for complex data structures that exhibit non-linear relationships or manifold structures. To overcome this limitation, future research could investigate alternative embedding spaces, such as hyperbolic or spherical spaces, which are better suited for capturing hierarchical or non-Euclidean relationships in the knowledge graph. By exploring different embedding spaces, SpherE can enhance its ability to represent diverse and intricate knowledge graph structures more effectively.