Context-aware Path Ranking for Knowledge Base Completion

TL;DR

This paper introduces a context-aware path ranking algorithm for knowledge base completion that improves scalability, reduces feature explosion, and enhances interpretability by leveraging entity semantics and selective path exploration.

Contribution

It proposes a novel C-PR algorithm that uses entity semantics and bidirectional random walk to efficiently discover relevant paths for KB completion.

Findings

C-PR discovers fewer, more relevant paths.

C-PR improves predictive accuracy over baselines.

C-PR enhances interpretability of features.

Abstract

Knowledge base (KB) completion aims to infer missing facts from existing ones in a KB. Among various approaches, path ranking (PR) algorithms have received increasing attention in recent years. PR algorithms enumerate paths between entity pairs in a KB and use those paths as features to train a model for missing fact prediction. Due to their good performances and high model interpretability, several methods have been proposed. However, most existing methods suffer from scalability (high RAM consumption) and feature explosion (trains on an exponentially large number of features) problems. This paper proposes a Context-aware Path Ranking (C-PR) algorithm to solve these problems by introducing a selective path exploration strategy. C-PR learns global semantics of entities in the KB using word embedding and leverages the knowledge of entity semantics to enumerate contextually relevant paths using bidirectional random walk. Experimental results on three large KBs show that the path features (fewer in number) discovered by C-PR not only improve predictive performance but also are more interpretable than existing baselines.