  Automatically constructing taxonomy finds many applications in e-commerce and
web search. One critical challenge is as data and business scope grow in real
applications, new concepts are emerging and needed to be added to the existing
taxonomy. Previous approaches focus on the taxonomy expansion, i.e. finding an
appropriate hypernym concept from the taxonomy for a new query concept. In this
paper, we formulate a new task, "taxonomy completion", by discovering both the
hypernym and hyponym concepts for a query. We propose Triplet Matching Network
(TMN), to find the appropriate <hypernym, hyponym> pairs for a given query
concept. TMN consists of one primal scorer and multiple auxiliary scorers.
These auxiliary scorers capture various fine-grained signals (e.g., query to
hypernym or query to hyponym semantics), and the primal scorer makes a holistic
prediction on <query, hypernym, hyponym> triplet based on the internal feature
representations of all auxiliary scorers. Also, an innovative channel-wise
gating mechanism that retains task-specific information in concept
representations is introduced to further boost model performance. Experiments
on four real-world large-scale datasets show that TMN achieves the best
performance on both taxonomy completion task and the previous taxonomy
expansion task, outperforming existing methods.
