Combining information extraction and human computing for crowdsourced knowledge acquisition

Automatic information extraction (IE) enables the construction of very large knowledge bases (KBs), with relational facts on millions of entities from text corpora and Web sources. However, such KBs contain errors and they are far from being complete. This motivates the need for exploiting human intelligence and knowledge using crowd-based human computing (HC) for assessing the validity of facts and for gathering additional knowledge. This paper presents a novel system architecture, called Higgins, which shows how to effectively integrate an IE engine and a HC engine. Higgins generates game questions where players choose or fill in missing relations for subject-relation-object triples. For generating multiple-choice answer candidates, we have constructed a large dictionary of entity names and relational phrases, and have developed specifically designed statistical language models for phrase relatedness. To this end, we combine semantic resources like WordNet, ConceptNet, and others with statistics derived from a large Web corpus. We demonstrate the effectiveness of Higgins for knowledge acquisition by crowdsourced gathering of relationships between characters in narrative descriptions of movies and books.