  Exploiting the appropriate inductive bias based on the knowledge of data is
essential for achieving good performance in statistical machine learning. In
practice, however, the domain knowledge of interest often provides information
on the relationship of data attributes only distantly, which hinders direct
utilization of such domain knowledge in popular regularization methods. In this
paper, we propose the knowledge-based distant regularization framework, in
which we utilize the distant information encoded in a knowledge graph for
regularization of probabilistic model estimation. In particular, we propose to
impose prior distributions on model parameters specified by knowledge graph
embeddings. As an instance of the proposed framework, we present the factor
analysis model with the knowledge-based distant regularization. We show the
results of preliminary experiments on the improvement of the generalization
capability of such model.
