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Knowledge bases (KBs) store rich yet heterogeneous entities and facts. Entityresolution (ER) aims to identify entities in KBs which refer to the samereal-world object. Recent studies have shown significant benefits of involvinghumans in the loop of ER. They often resolve entities with pairwise similaritymeasures over attribute values and resort to the crowds to label uncertainones. However, existing methods still suffer from high labor costs andinsufficient labeling to some extent. In this paper, we propose a novelapproach called crowdsourced collective ER, which leverages the relationshipsbetween entities to infer matches jointly rather than independently.Specifically, it iteratively asks human workers to label picked entity pairsand propagates the labeling information to their neighbors in distance. Duringthis process, we address the problems of candidate entity pruning,probabilistic propagation, optimal question selection and error-tolerant truthinference. Our experiments on real-world datasets demonstrate that, comparedwith state-of-the-art methods, our approach achieves superior accuracy withmuch less labeling.

Crowdsourced Collective Entity Resolution with Relational Match Propagation