  Taxonomy expansion is the process of incorporating a large number of
additional nodes (i.e., "queries") into an existing taxonomy (i.e., "seed"),
with the most important step being the selection of appropriate positions for
each query. Enormous efforts have been made by exploring the seed's structure.
However, existing approaches are deficient in their mining of structural
information in two ways: poor modeling of the hierarchical semantics and
failure to capture directionality of is-a relation. This paper seeks to address
these issues by explicitly denoting each node as the combination of inherited
feature (i.e., structural part) and incremental feature (i.e., supplementary
part). Specifically, the inherited feature originates from "parent" nodes and
is weighted by an inheritance factor. With this node representation, the
hierarchy of semantics in taxonomies (i.e., the inheritance and accumulation of
features from "parent" to "child") could be embodied. Additionally, based on
this representation, the directionality of is-a relation could be easily
translated into the irreversible inheritance of features. Inspired by the
Darmois-Skitovich Theorem, we implement this irreversibility by a non-Gaussian
constraint on the supplementary feature. A log-likelihood learning objective is
further utilized to optimize the proposed model (dubbed DNG), whereby the
required non-Gaussianity is also theoretically ensured. Extensive experimental
results on two real-world datasets verify the superiority of DNG relative to
several strong baselines.
