  Taxonomy completion, enriching existing taxonomies by inserting new concepts
as parents or attaching them as children, has gained significant interest.
Previous approaches embed concepts as vectors in Euclidean space, which makes
it difficult to model asymmetric relations in taxonomy. In addition, they
introduce pseudo-leaves to convert attachment cases into insertion cases,
leading to an incorrect bias in network learning dominated by numerous
pseudo-leaves. Addressing these, our framework, TaxBox, leverages box
containment and center closeness to design two specialized geometric scorers
within the box embedding space. These scorers are tailored for insertion and
attachment operations and can effectively capture intrinsic relationships
between concepts by optimizing on a granular box constraint loss. We employ a
dynamic ranking loss mechanism to balance the scores from these scorers,
allowing adaptive adjustments of insertion and attachment scores. Experiments
on four real-world datasets show that TaxBox significantly outperforms previous
methods, yielding substantial improvements over prior methods in real-world
datasets, with average performance boosts of 6.7%, 34.9%, and 51.4% in MRR,
Hit@1, and Prec@1, respectively.
