Article ID Journal Published Year Pages File Type
4944745 Information Sciences 2017 19 Pages PDF
Abstract
In this paper, we present an efficient algorithm to integrate two graphs collected from different sources using crowdsourcing systems. Given two graphs, we repeatedly select a query node from a graph and request a human annotator to find its matching node from the other graph, which is considered to be the one indicating the same entity as the query node. The proposed method is to choose the query nodes that would increase the precision the most if it is labeled. By experiments with both the simulated answers and the labels collected by real crowdsourcing, we show that our algorithm finds more accurate graph matches with a smaller cost for crowdsourcing than the baseline algorithms.
Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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