User-friendly Comparison of Similarity Algorithms on Wikidata
  While the similarity between two concept words has been evaluated and studied
for decades, much less attention has been devoted to algorithms that can
compute the similarity of nodes in very large knowledge graphs, like Wikidata.
To facilitate investigations and head-to-head comparisons of similarity
algorithms on Wikidata, we present a user-friendly interface that allows
flexible computation of similarity between Qnodes in Wikidata. At present, the
similarity interface supports four algorithms, based on: graph embeddings
(TransE, ComplEx), text embeddings (BERT), and class-based similarity. We
demonstrate the behavior of the algorithms on representative examples about
semantically similar, related, and entirely unrelated entity pairs. To support
anticipated applications that require efficient similarity computations, like
entity linking and recommendation, we also provide a REST API that can compute
most similar neighbors for any Qnode in Wikidata.
