  Taxonomies are of great value to many knowledge-rich applications. As the
manual taxonomy curation costs enormous human effects, automatic taxonomy
construction is in great demand. However, most existing automatic taxonomy
construction methods can only build hypernymy taxonomies wherein each edge is
limited to expressing the "is-a" relation. Such a restriction limits their
applicability to more diverse real-world tasks where the parent-child may carry
different relations. In this paper, we aim to construct a task-guided taxonomy
from a domain-specific corpus and allow users to input a "seed" taxonomy,
serving as the task guidance. We propose an expansion-based taxonomy
construction framework, namely HiExpan, which automatically generates key term
list from the corpus and iteratively grows the seed taxonomy. Specifically,
HiExpan views all children under each taxonomy node forming a coherent set and
builds the taxonomy by recursively expanding all these sets. Furthermore,
HiExpan incorporates a weakly-supervised relation extraction module to extract
the initial children of a newly-expanded node and adjusts the taxonomy tree by
optimizing its global structure. Our experiments on three real datasets from
different domains demonstrate the effectiveness of HiExpan for building
task-guided taxonomies.
