  Most of previous work in knowledge base (KB) completion has focused on the
problem of relation extraction. In this work, we focus on the task of inferring
missing entity type instances in a KB, a fundamental task for KB competition
yet receives little attention. Due to the novelty of this task, we construct a
large-scale dataset and design an automatic evaluation methodology. Our
knowledge base completion method uses information within the existing KB and
external information from Wikipedia. We show that individual methods trained
with a global objective that considers unobserved cells from both the entity
and the type side gives consistently higher quality predictions compared to
baseline methods. We also perform manual evaluation on a small subset of the
data to verify the effectiveness of our knowledge base completion methods and
the correctness of our proposed automatic evaluation method.
