# Modeling Relation Paths for Representation Learning of Knowledge Bases

## TL;DR

This paper introduces a path-based representation learning model for knowledge bases that considers multi-step relation paths, improving inference and completion tasks by measuring path reliability and semantic composition.

## Contribution

It proposes a novel model that incorporates relation paths as translations, with a path-constraint resource allocation algorithm and semantic composition for improved knowledge base embedding.

## Key findings

- Significant improvements in knowledge base completion
- Enhanced relation extraction from text
- Effective measurement of relation path reliability

## Abstract

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. (2) We represent relation paths via semantic composition of relation embeddings. Experimental results on real-world datasets show that, as compared with baselines, our model achieves significant and consistent improvements on knowledge base completion and relation extraction from text.