AI

Using Entity Information from a Knowledge Base to Improve Relation Extraction

Abstract

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