Modeling Relation Paths for Representation Learning of Knowledge Bases

Understand

Representation learning of knowledge bases (KBs) aims to embed both entities and relations into a low-dimensional space.

• Most existing methods only consider direct relations in representation learning.
• We argue that multiple-step relation paths also contain rich inference patterns between entities, and propose a path-based representation learning model.
• This model considers relation paths as translations between entities for representation learning, and addresses two key challenges: (1) Since not all relation paths are reliable, we design a path-constraint resource allocation algorithm to measure the reliability of relation paths.