Personalized classifiers: Evolving a classifier from a large reference Knowledge Graph

Abstract

Identifying the right choice of categories for organizing and representing a large digital library of documents is a challenging task. A completely automated approach to category creation from the underlying collection could be prone to noise. On the other hand, an absolutely manual approach to the creation of categories could be cumbersome and expensive. Through this work, we propose an intermediate solution, in which, a global, collaboratively-developed Knowledge Graph of categories can be adapted to a local document categorization problem effectively. We model our classification problem as that of inferring structured labels in an Associative Markov Network meta-model over SVMs, where the label space is derived from a large global category graph. We propose a joint Active Learning model over the label and the document spaces in order to incorporate active labeling feedback from the users to train the model parameters. © 2014 ACM.