| Abstract | Many domains are best characterized as anaffiliation network describing a set of actors
and a set of events interlinked together in
a variety of relationships. Relational clas-
sification in these domains requires the col-
lective classification of both entities (actors
and events) and relationships. We investigate
the use of relational Markov networks (RMN)
for relational classification in affiliation net-
works. In this paper, we introduce a novel
dataset, Profile in Terror (PIT) knowledge
base, that provides a rich source of various af-
filiation networks. We study two tasks, entity
labeling and relationship labeling. We high-
light several important issues concerning the
effectiveness of relational classification. Our
results show that the PIT dataset has a rich
source of relational structure and therefore
it is a useful dataset for statisical relational
network learning community.
|
