Aggregating responses from crowd workers is a fundamental task in the process of crowdsourcing. In cases where a few experts are overwhelmed by a large number of non-experts, most answer aggregation algorithms such as the majority voting fail to identify the correct answers. Therefore, it is crucial to extract reliable experts from the crowd workers. In this study, we introduce the notion of "expert core", which is a set of workers that is very unlikely to contain a non-expert. We design a graph-mining-based efficient algorithm that exactly computes the expert core. To answer the aggregation task, we propose two types of algorithms. The first one incorporates the expert core into existing answer aggregation algorithms such as the majority voting, whereas the second one utilizes information provided by the expert core extraction algorithm pertaining to the reliability of workers. We then give a theoretical justification for the first type of algorithm. Computational experiments using synthetic and real-world datasets demonstrate that our proposed answer aggregation algorithms outperform state-of-the-art algorithms.

本文提出了基于图的挖掘方法来有效计算可靠众包工人的“专家核心”，并提出了一种将“专家核心”与现有答案聚合算法结合的方法以及一种利用“专家核心”提取算法所提供的与工人可靠性相关的信息的算法，在人工合成和真实数据集上的计算实验表明，我们提出的答案聚合算法优于现有算法。

图挖掘与众包相遇：为答案聚合挖掘专家