  Automatic construction of a taxonomy supports many applications in
e-commerce, web search, and question answering. Existing taxonomy expansion or
completion methods assume that new concepts have been accurately extracted and
their embedding vectors learned from the text corpus. However, one critical and
fundamental challenge in fixing the incompleteness of taxonomies is the
incompleteness of the extracted concepts, especially for those whose names have
multiple words and consequently low frequency in the corpus. To resolve the
limitations of extraction-based methods, we propose GenTaxo to enhance taxonomy
completion by identifying positions in existing taxonomies that need new
concepts and then generating appropriate concept names. Instead of relying on
the corpus for concept embeddings, GenTaxo learns the contextual embeddings
from their surrounding graph-based and language-based relational information,
and leverages the corpus for pre-training a concept name generator.
Experimental results demonstrate that GenTaxo improves the completeness of
taxonomies over existing methods.
