Structured Prediction using Voted Conditional Random Fields

Knowledge bases are useful in the validation of automatically extracted information, and for hypothesis selection during the extraction process. Building knowledge bases is a difficult task and the process is bound to miss facts. Therefore, the existence of facts can be estimated using link prediction, i.e., by solving the structured prediction problem. It has been shown that combining directly observable features with latent features increases performance. Observable features include, e.g., the presence of another chain of facts leading to the same endpoint. Latent features include, e.g, properties that are not modelled by facts on the form subject-predicate-object, such as being a good actor. Observable graph features are modelled using the Path Ranking Algorithm, and latent features using the bilinear RESCAL model. Voted Conditional Random Fields can be used to combine feature families while taking into account their complexity to minimize the risk of training a poor predictor. We propose a combined model fusing these theories together with a complexity analysis of the feature families used. In addition, two simple feature families are constructed to model neighborhood properties. The model we propose captures useful features for link prediction, but needs further evaluation to guarantee efficient learning. Finally, suggestions for experiments and other feature families are given.