Learning to Map Wikidata Entities To Predefined Topics

Recently much progress has been made in entity disambiguation and linking systems (EDL). Given a piece of text, EDL links words and phrases to entities in a knowledge base, where each entity defines a specific concept. Although extracted entities are informative, they are often too specific to be used directly by many applications. These applications usually require text content to be represented with a smaller set of predefined concepts or topics, belonging to a topical taxonomy, that matches their exact needs. In this study, we aim to build a system that maps Wikidata entities to such predefined topics. We explore a wide range of methods that map entities to topics, including GloVe similarity, Wikidata predicates, Wikipedia entity definitions, and entity-topic co-occurrences. These methods often predict entity-topic mappings that are reliable, i.e., have high precision, but tend to miss most of the mappings, i.e., have low recall. Therefore, we propose an ensemble system that effectively combines individual methods and yields much better performance, comparable with human annotators.