TaxoSBERT: Unsupervised Taxonomy Expansion Through Expressive Semantic Similarity

Knowledge graphs are crucial resources for a large set of document management tasks, such as text retrieval and classification as well as natural language inference. Standard examples are large-scale lexical semantic graphs, such as WordNet, useful for text tagging or sentence disambiguation purposes. The dynamics of lexical taxonomies is a critical problem as they need to be maintained to follow the language evolution across time. Taxonomy expansion, in this sense, becomes a critical semantic task, as it allows for an extension of existing resources with new properties but also to create new entries, i.e. taxonomy concepts, when necessary. Previous work on this topic suggests the use of neural learning methods able to make use of the underlying taxonomy graph as a source of training evidence. This can be done by graph-based learning, where nets are trained to encode the underlying knowledge graph and to predict appropriate inferences. This paper presents TaxoSBERT as a simple and effective way to model the taxonomy expansion problem as a retrieval task. It combines a robust semantic similarity measure and taxonomy-driven re-rank strategies. This method is unsupervised, the adopted similarity measures are trained on (large-scale) resources out of a target taxonomy and are extremely efficient. The experimental evaluation with respect to two taxonomies shows surprising results, improving far more complex state-of-the-art methods.