Towards best practices for crowdsourcing ontology alignment benchmarks

Ontology alignment systems establish the links between ontologies that enable knowledge from various sources and domains to be used by applications in many different ways. Unfortunately, these systems are not perfect. Currently the results of even the best-performing alignment systems need to be manually verified in order to be fully trusted. Ontology alignment researchers have turned to crowdsourcing platforms such as Amazon’s Mechanical Turk to accomplish this. However, there has been little systematic analysis of the accuracy of crowdsourcing for alignment verification and the establishment of best practices. In this work, we analyze the impact of the presentation of the context of potential matches and the way in which the question is presented to workers on the accuracy of crowdsourcing for alignment verification.