Label and Link Prediction in Relational Data
Many real-world domains are relational in nature, consisting of a set of entities linked to each other in complex ways. Two important tasks in such data are predicting entity labels and links between entities. We present a flexible framework that builds on (conditional) Markov networks and successfully addresses both tasks by capturing complex dependencies in the data. These models can compactly represent probabilistic patterns over subgraph structures and use them to predict labels and links effectively. We show how to train these models, and how to use approximate probabilistic inference over the learned model for collective classification of multiple related entities and links. We evaluate our framework on several relational datasets, including university webpages and social networks. Our approach achieves significantly better performance than flat classification, which attempts to predict each label and link in isolation.