  Taxonomy is not only a fundamental form of knowledge representation, but also
crucial to vast knowledge-rich applications, such as question answering and web
search. Most existing taxonomy construction methods extract hypernym-hyponym
entity pairs to organize a "universal" taxonomy. However, these generic
taxonomies cannot satisfy user's specific interest in certain areas and
relations. Moreover, the nature of instance taxonomy treats each node as a
single word, which has low semantic coverage. In this paper, we propose a
method for seed-guided topical taxonomy construction, which takes a corpus and
a seed taxonomy described by concept names as input, and constructs a more
complete taxonomy based on user's interest, wherein each node is represented by
a cluster of coherent terms. Our framework, CoRel, has two modules to fulfill
this goal. A relation transferring module learns and transfers the user's
interested relation along multiple paths to expand the seed taxonomy structure
in width and depth. A concept learning module enriches the semantics of each
concept node by jointly embedding the taxonomy and text. Comprehensive
experiments conducted on real-world datasets show that Corel generates
high-quality topical taxonomies and outperforms all the baselines
significantly.
