Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4944745 | Information Sciences | 2017 | 19 Pages |
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.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Younghoon Kim, Woohwan Jung, Kyuseok Shim,