A Bayesian Nonparametric Model for Joint Relation Integration and Domain Clustering
Dazhuo Li, Fahim Mohammad, Eric Christian Rouchka · 2010
Relational databases provide unprecedented opportunities for knowledge discovery. Various approaches have been proposed to infer structures over entity types and predict relationships among elements of these types. However, discovering structures beyond the entity type level, e.g. clustering over relation concepts, remains a challenging task. We present a Bayesian nonparametric model for joint relation and domain clustering. The model can automatically infer the number of relation clusters, which is particularly important in novel cases where little prior knowledge is known about the number of relation clusters. The approach is applied to clustering various relations in a gene database.