High quality taxonomies play a critical role in various domains
such as e-commerce, web search and ontology engineering. While
there has been extensive work on expanding taxonomies from
externally mined data, there has been less attention paid to enriching taxonomies by exploiting existing concepts and structure
within the taxonomy. ...
High quality taxonomies play a critical role in various domains
such as e-commerce, web search and ontology engineering. While
there has been extensive work on expanding taxonomies from
externally mined data, there has been less attention paid to enriching taxonomies by exploiting existing concepts and structure
within the taxonomy. In this work, we show the usefulness of this
kind of enrichment, and explore its viability with a new taxonomy
completion system ICON (Implicit CONcept Insertion). ICON generates new concepts by identifying implicit concepts based on the
existing concept structure, generating names for such concepts
and inserting them in appropriate positions within the taxonomy.
ICON integrates techniques from entity retrieval, text summary,
and subsumption prediction; this modular architecture offers high
flexibility while achieving state-of-the-art performance. We have
evaluated ICON on two e-commerce taxonomies, and the results
show that it offers significant advantages over strong baselines including recent taxonomy completion models and the large language
model, ChatGPT.
