User-friendly Comparison of Similarity Algorithms on Wikidata

Understand

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.