Locally Adaptive Translation for Knowledge Graph Embedding
Table 4: Evaluation results of triple classification. (%)
['Data sets', 'WN11', 'FB13', 'FB15K']
All parameters are determined on the validation set. The optimal setting are: λ=0.001, d=220, B=120, μ=0.5 and taking L1 as dissimilarity on WN11; λ=0.001, d=50, B=480, μ=0.5 and taking L1 as dissimilarity on FB13. On WN11, TransA outperforms the other methods. On FB13, the method NTN is shown more powerful. This is consistent with the results in previous literature On FB15K, TransA also performs the best. Since FB13 is much denser to FB15K, NTN is more expressive on dense graph. On sparse graph, TransA is superior to other state-of-the-art embedding methods.