Taxonomy extraction using knowledge graph embeddings and hierarchical clustering

While high-quality taxonomies are essential to the Semantic Web, building them for large knowledge graphs is an expensive process. Likewise, creating taxonomies that accurately reflect the content of dynamic knowledge graphs is another challenge. In this paper, we propose a method to automatically extract a taxonomy from knowledge graph embeddings, and evaluate it on DBpedia. Our approach produces a taxonomy by leveraging the type information contained in the graph and the tree-like structure of an unsupervised hierarchical clustering performed over entity embeddings. We then extend our method with an axiom induction mechanism which allows us to identify new classes from the data and describe them with logical axioms, thus leading to expressive taxonomy extraction.

Mots clés

• Knowledge graph embeddings
• expressive taxonomies
• ontology learning
• hierarchical clustering