Joint Matrix-Tensor Factorization for Knowledge Base Inference
  While several matrix factorization (MF) and tensor factorization (TF) models
have been proposed for knowledge base (KB) inference, they have rarely been
compared across various datasets. Is there a single model that performs well
across datasets? If not, what characteristics of a dataset determine the
performance of MF and TF models? Is there a joint TF+MF model that performs
robustly on all datasets? We perform an extensive evaluation to compare popular
KB inference models across popular datasets in the literature. In addition to
answering the questions above, we remove a limitation in the standard
evaluation protocol for MF models, propose an extension to MF models so that
they can better handle out-of-vocabulary (OOV) entity pairs, and develop a
novel combination of TF and MF models. We also analyze and explain the results
based on models and dataset characteristics. Our best model is robust, and
obtains strong results across all datasets.
