A Benchmarking Study ofEmbedding-based Entity Alignment for Knowledge Graphs
Table 4: Common hyper-parameters for all the approaches
['[EMPTY]', '15K', '100K']
Comparative approaches and settings. We evaluate all the embedding-based entity alignment approaches implemented in OpenEA. To make a fair comparison, we try our best efforts to unify the experiment settings. So, we use a fixed batch size for relation triples to avoid its interference. For other settings specific to each approach, we follow the reported details in literature as carefully as we can. For the key hyper-parameters and the unreported ones, we try our best to tune them. For example, we constrain the L2-norm of entity embeddings to 1 for many approaches, e.g., IMUSE, because we find that such normalization yields better results. All hyper-parameter settings of each approach on our datasets are available online.