  Relevance search is to find top-ranked entities in a knowledge graph (KG)
that are relevant to a query entity. Relevance is ambiguous, particularly over
a schema-rich KG like DBpedia which supports a wide range of different
semantics of relevance based on numerous types of relations and attributes. As
users may lack the expertise to formalize the desired semantics, supervised
methods have emerged to learn the hidden user-defined relevance from
user-provided examples. Along this line, in this paper we propose a novel
generative model over KGs for relevance search, named GREASE. The model applies
to meta-path based relevance where a meta-path characterizes a particular type
of semantics of relating the query entity to answer entities. It is also
extended to support properties that constrain answer entities. Extensive
experiments on two large-scale KGs demonstrate that GREASE has advanced the
state of the art in effectiveness, expressiveness, and efficiency.
