Context-aware Path Ranking for Knowledge Base Completion

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

Using entity information from a knowledge base to improve relation extraction

Relation extraction is the task of ex-tracting predicate-argument relationships between entities from natural language text. This paper investigates whether back-ground information about entities avail-able in knowledge bases such as FreeBase can be used to improve the accuracy of a state-of-the-art relation extraction sys-tem. We describe a simple and effective way of incorporating FreeBase’s notable types into a state-of-the-art relation extrac-tion system (Riedel et al., 2013). Experi-mental results show that our notable type-based system achieves an average 7.5% weighted MAP score improvement. To understand where the notable type infor-mation contributes the most, we perform a series of ablation experiments. Results show that the notable type information im-proves relation extraction more than NER labels alone across a wide range of entity types and relations.