Taxonomies, which organize knowledge hierarchically, support
various practical web applications such as product navigation in
online shopping and user profle tagging on social platforms. Given
the continued and rapid emergence of new entities, maintaining a
comprehensive taxonomy in a timely manner through human annotation
is prohibitively expensive. Therefore, expanding a taxonomy
automatically with new entities is essential. Most existing methods
for expanding taxonomies encode entities into vector embeddings
(i.e., single points). However, we argue that vectors are insufcient
to model the “is-a” hierarchy in taxonomy (asymmetrical relation),
because two points can only represent pairwise similarity (symmetrical
relation). To this end, we propose to project taxonomy
entities into boxes (i.e., hyperrectangles). Two boxes can be "contained",
"disjoint" and "intersecting", thus naturally representing an
asymmetrical taxonomic hierarchy. Upon box embeddings, we propose
a novel model BoxTaxo for taxonomy expansion. The core of
BoxTaxo is to learn boxes for entities to capture their child-parent
hierarchies. To achieve this, BoxTaxo optimizes the box embeddings
from a joint view of geometry and probability. BoxTaxo also
ofers an easy and natural way for inference: examine whether the
box of a given new entity is fully enclosed inside the box of a candidate
parent from the existing taxonomy. Extensive experiments on
two benchmarks demonstrate the efectiveness of BoxTaxo compared
to vector based models.
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Free, publicly-accessible full text available April 30, 2024
