Context-aware Path Ranking for Knowledge Base Completion
This work addresses efficiency and interpretability issues in knowledge base completion, which is important for applications like semantic search and question answering, but it is incremental as it builds on existing path ranking methods.
The paper tackles scalability and feature explosion problems in path ranking algorithms for knowledge base completion by proposing a context-aware method that uses entity semantics to selectively explore paths, resulting in improved predictive performance and interpretability on three large knowledge bases.
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.