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Knowledge graph embedding models (KGEMs) developed for link prediction learnvector representations for graph entities, known as embeddings. A common tacitassumption is the KGE entity similarity assumption, which states that theseKGEMs retain the graph's structure within their embedding space, i.e., positionsimilar entities close to one another. This desirable property make KGEMswidely used in downstream tasks such as recommender systems or drugrepurposing. Yet, the alignment of graph similarity with embedding spacesimilarity has rarely been formally evaluated. Typically, KGEMs are assessedbased on their sole link prediction capabilities, using ranked-based metricssuch as Hits@K or Mean Rank. This paper challenges the prevailing assumptionthat entity similarity in the graph is inherently mirrored in the embeddingspace. Therefore, we conduct extensive experiments to measure the capability ofKGEMs to cluster similar entities together, and investigate the nature of theunderlying factors. Moreover, we study if different KGEMs expose a differentnotion of similarity. Datasets, pre-trained embeddings and code are availableat: https://github.com/nicolas-hbt/similar-embeddings.

Do Similar Entities have Similar Embeddings?