  One of the key obstacles in making learning protocols realistic in
applications is the need to supervise them, a costly process that often
requires hiring domain experts. We consider the framework to use the world
knowledge as indirect supervision. World knowledge is general-purpose
knowledge, which is not designed for any specific domain. Then the key
challenges are how to adapt the world knowledge to domains and how to represent
it for learning. In this paper, we provide an example of using world knowledge
for domain dependent document clustering. We provide three ways to specify the
world knowledge to domains by resolving the ambiguity of the entities and their
types, and represent the data with world knowledge as a heterogeneous
information network. Then we propose a clustering algorithm that can cluster
multiple types and incorporate the sub-type information as constraints. In the
experiments, we use two existing knowledge bases as our sources of world
knowledge. One is Freebase, which is collaboratively collected knowledge about
entities and their organizations. The other is YAGO2, a knowledge base
automatically extracted from Wikipedia and maps knowledge to the linguistic
knowledge base, WordNet. Experimental results on two text benchmark datasets
(20newsgroups and RCV1) show that incorporating world knowledge as indirect
supervision can significantly outperform the state-of-the-art clustering
algorithms as well as clustering algorithms enhanced with world knowledge
features.
