Entity resolution （ER） aims to identify whether two entities in an ER task refer to the same real-world thing.Crowd ER uses humans, in addition to machine algorithms, to obtain the truths of ER tasks. However, inacc...Entity resolution （ER） aims to identify whether two entities in an ER task refer to the same real-world thing.Crowd ER uses humans, in addition to machine algorithms, to obtain the truths of ER tasks. However, inaccurate orerroneous results are likely to be generated when humans give unreliable judgments. Previous studies have found thatcorrectly estimating human accuracy or expertise in crowd ER is crucial to truth inference. However, a large number ofthem assume that humans have consistent expertise over all the tasks, and ignore the fact that humans may have variedexpertise on different topics （e.g., music versus sport）. In this paper, we deal with crowd ER in the Semantic Web area.We identify multiple topics of ER tasks and model human expertise on different topics. Furthermore, we leverage similartask clustering to enhance the topic modeling and expertise estimation. We propose a probabilistic graphical model thatcomputes ER task similarity, estimates human expertise, and infers the task truths in a unified framework. Our evaluationresults on real-world and synthetic datasets show that, compared with several state-of-the-art approaches, our proposedmodel achieves higher accuracy on the task truth inference and is more consistent with the human real expertise.展开更多
基金This work was supported by the National Natural Science Foundation of China under Grant Nos. 61872172 and 61772264.
文摘Entity resolution （ER） aims to identify whether two entities in an ER task refer to the same real-world thing.Crowd ER uses humans, in addition to machine algorithms, to obtain the truths of ER tasks. However, inaccurate orerroneous results are likely to be generated when humans give unreliable judgments. Previous studies have found thatcorrectly estimating human accuracy or expertise in crowd ER is crucial to truth inference. However, a large number ofthem assume that humans have consistent expertise over all the tasks, and ignore the fact that humans may have variedexpertise on different topics （e.g., music versus sport）. In this paper, we deal with crowd ER in the Semantic Web area.We identify multiple topics of ER tasks and model human expertise on different topics. Furthermore, we leverage similartask clustering to enhance the topic modeling and expertise estimation. We propose a probabilistic graphical model thatcomputes ER task similarity, estimates human expertise, and infers the task truths in a unified framework. Our evaluationresults on real-world and synthetic datasets show that, compared with several state-of-the-art approaches, our proposedmodel achieves higher accuracy on the task truth inference and is more consistent with the human real expertise.
