%0 Journal Article %J ICML Workshop on Statistical Network Analysis %D 2006 %T Entity and relationship labeling in affiliation networks %A Zhao,B. %A Sen,P. %A Getoor, Lise %X 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. %B ICML Workshop on Statistical Network Analysis %8 2006/// %G eng