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 entitypairs 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