Incorporating Selectional Preferences in Multi-hop Relation Extraction
Rajarshi Das, Arvind Neelakantan, David Belanger, Andrew McCallum · 2016
Relation extraction is one of the core challenges in automated knowledge base construction. One line of approach for relation extraction is to perform multi-hop reasoning on the paths connecting an entity pair to infer new relations. While these methods have been successfully applied for knowledge base completion, they do not utilize the entity or the entity type information to make predictions. In this work, we incorporate selectional preferences, i.e., relations enforce constraints on the allowed entity types for the candidate entities, to multi-hop relation extraction by including entity type information. We achieve a 17.67% (relative) improvement in MAP score in a relation extraction task when compared to a method that does not use entity type information.