Knowledge-Based Distant Regularization in Learning Probabilistic Models
This addresses the challenge of leveraging indirect domain knowledge in machine learning, though it appears incremental as it builds on existing regularization and knowledge graph methods.
The paper tackles the problem of incorporating distant domain knowledge, encoded in knowledge graphs, into probabilistic model regularization, and shows preliminary experiments indicating improved generalization capability.
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.