  Knowledge bases (KBs) store rich yet heterogeneous entities and facts. Entity
resolution (ER) aims to identify entities in KBs which refer to the same
real-world object. Recent studies have shown significant benefits of involving
humans in the loop of ER. They often resolve entities with pairwise similarity
measures over attribute values and resort to the crowds to label uncertain
ones. However, existing methods still suffer from high labor costs and
insufficient labeling to some extent. In this paper, we propose a novel
approach called crowdsourced collective ER, which leverages the relationships
between entities to infer matches jointly rather than independently.
Specifically, it iteratively asks human workers to label picked entity pairs
and propagates the labeling information to their neighbors in distance. During
this process, we address the problems of candidate entity pruning,
probabilistic propagation, optimal question selection and error-tolerant truth
inference. Our experiments on real-world datasets demonstrate that, compared
with state-of-the-art methods, our approach achieves superior accuracy with
much less labeling.
