  Text-rich heterogeneous information networks (text-rich HINs) are ubiquitous
in real-world applications. Hypernymy, also known as is-a relation or
subclass-of relation, lays in the core of many knowledge graphs and benefits
many downstream applications. Existing methods of hypernymy discovery either
leverage textual patterns to extract explicitly mentioned hypernym-hyponym
pairs, or learn a distributional representation for each term of interest based
its context. These approaches rely on statistical signals from the textual
corpus, and their effectiveness would therefore be hindered when the signals
from the corpus are not sufficient for all terms of interest. In this work, we
propose to discover hypernymy in text-rich HINs, which can introduce additional
high-quality signals. We develop a new framework, named HyperMine, that
exploits multi-granular contexts and combines signals from both text and
network without human labeled data. HyperMine extends the definition of context
to the scenario of text-rich HIN. For example, we can define typed nodes and
communities as contexts. These contexts encode signals of different
granularities and we feed them into a hypernymy inference model. HyperMine
learns this model using weak supervision acquired based on high-precision
textual patterns. Extensive experiments on two large real-world datasets
demonstrate the effectiveness of HyperMine and the utility of modeling context
granularity. We further show a case study that a high-quality taxonomy can be
generated solely based on the hypernymy discovered by HyperMine.
