| Abstract: | Collective Classification is the process of labeling
instances in a graph using both instance attribute information
and information about relations between instances. While
several Collective Classification Algorithms have been well
studied, the use of Markov Logic Networks (MLNs) remains
largely untested. MLNs pair first order logic
statements with a numerical weight. With properly
assigned weights, these rules may be used to infer
class labels from evidence stated as logic statements. Our
study evaluated MLNs against other Collective Classification
algorithms on both synthetic data and real data from the CiteSeer
dataset. As a whole, we encountered inconsistent and often poor
performance with MLNs, especially on synthetic data where other
Collective Classification algorithms performed well. |