User-friendly Comparison of Similarity Algorithms on Wikidata

TL;DR

This paper introduces a user-friendly interface and REST API for comparing different similarity algorithms on Wikidata, enabling efficient and flexible similarity computations for large knowledge graphs.

Contribution

It presents a novel interface supporting multiple similarity algorithms on Wikidata, facilitating research and applications like entity linking and recommendations.

Findings

Supported algorithms include graph embeddings, text embeddings, and class-based similarity.

Demonstrated behavior of algorithms on various semantic similarity examples.

Provided an API for efficient similarity computations in large knowledge graphs.

Abstract

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.