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Most of previous work in knowledge base (KB) completion has focused on theproblem of relation extraction. In this work, we focus on the task of inferringmissing entity type instances in a KB, a fundamental task for KB competitionyet receives little attention. Due to the novelty of this task, we construct alarge-scale dataset and design an automatic evaluation methodology. Ourknowledge base completion method uses information within the existing KB andexternal information from Wikipedia. We show that individual methods trainedwith a global objective that considers unobserved cells from both the entityand the type side gives consistently higher quality predictions compared tobaseline methods. We also perform manual evaluation on a small subset of thedata to verify the effectiveness of our knowledge base completion methods andthe correctness of our proposed automatic evaluation method.

Inferring Missing Entity Type Instances for Knowledge Base Completion: New Dataset and Methods