Integration of graphs from different data sources using crowdsourcing

Data integration is the process of identifying pairs of records from different databases that refer to the same entity in the real world. It has been extensively studied with regard to entity resolution, record linkage, duplicate detection or network alignment. With the increasing use of crowdsourcing platforms as a means of assessing queries manually at low cost, many studies have begun to consider ways to exploit crowdsourcing systems for efficient data integration. 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. (C) 2017 Elsevier Inc. All rights reserved.This research was supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2012M3C4 A7033342). This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2016-H8501-16-1013) supervised by the IITP (Institute for Information & communication Technology Promotion). This work was also supported by Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20153010011980)