Locally Adaptive Translation for Knowledge Graph Embedding
Table 1: Different choices of optimal loss functions and the predictive performances over three data sets Subset1, Subset2 and FB15K, where fr(h,t)=∥h+r−t∥22, (h,r,t) is a triple in knowledge graph, and (h′,r,t′) is incorrect triple.
['Data sets', 'Optimal loss function', 'Mean Rank Raw', 'Mean Rank Filter']
To verify this, we construct knowledge graphs with different locality. We simply partition a knowledge graph into different subgraphs in a uniform manner. Each subgraph contains different types of relations and their corresponding entities. Moreover, different subgraphs have the identical number of relations for the sake of balance of the number of entities. We claim that over different subgraphs, the optimal margin-based loss function may be different in terms of the margin. To validate this point, we perform the embedding method And we partition FB15K into five subsets with equal size of relations. For example, one subset, named Subset1, contains 13,666 entities and 269 relations. Another subset, named Subset2, has 13603 entities and 269 relations. More precisely, they take different values of margin as 3 and 2, respectively. This suggests that knowledge embedding of different knowledge graphs with a global setting of loss function can not well represent the locality of knowledge graphs, and it is indispensable to propose a locality sensitive loss function with different margins.