  Heterogeneous information network (HIN) has shown its power of modeling real
world data as a multi-typed entity-relation graph. Meta-path is the key
contributor to this power since it enables inference by capturing the
proximities between entities via rich semantic links. Previous HIN studies ask
users to provide either 1) the meta-path(s) directly or 2) biased examples to
generate the meta-path(s). However, lots of HINs (e.g., YAGO2 and Freebase)
have rich schema consisting of a sophisticated and large number of types of
entities and relations. It is impractical for users to provide the meta-path(s)
to support the large scale inference, and biased examples will result in
incorrect meta-path based inference, thus limit the power of the meta-path. In
this paper, we propose a meta-path constrained inference framework to further
release the ability of the meta-path, by efficiently learning the HIN inference
patterns via a carefully designed tree structure; and performing unbiased
random walk inference with little user guidance. The experiment results on
YAGO2 and DBLP datasets show the state-of-the-art performance of the meta-path
constrained inference framework.
