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      "@value" : "From a process point of view, [41] introduced a methodology for publishing Linked Data. They dis- cussed activities which theoretically could be subject to crowdsourcing, but did not discuss such aspects explicitly. Similarly, [25] tried to map ontology engineering methodologies to Linked Data practice, drawing on insights from interviews with practitioners and quantitative analysis. A more focused account of the use of human and crowd intelligence in Linked Data man- agement is offered in [36]. The authors investigated several technically oriented scenarios in order to identify lower-level tasks and analyze the extent to which they can be feasibly automated. In this context, feasibility referred primarily to the trade-off between the effort associated with the usage of a given tool targeting automation - including aspects such as getting familiar with the tool, but more importantly creating training data sets and examples, configuring the tool and validating (intermediary) results - and the quality of the outcomes. The fundamental question the work attempted to answer was related to ours, though not focused on quality assurance and repair – their aim was come up with patterns for human and machine-driven computation, which could service semantic data management scenarios effectively. This was also at the core of [35], which took the main findings of this analysis a step further and proposed a methodology to build incentivized Semantic Web applications, including guidelines for mechanism design which are compatible to our fix-find-verify workflow. They have also analyzed motivators and incentives for several types of Semantic Web tasks, from ontology population to se- mantic annotation."
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