Matches in Nanopublications for { ?s ?p "In order to understand the previous results, we analyzed the performance of experts and workers at a more fine-grained level. We calculated the frequency of occurrences of datatypes in the assessed triples (see Figure 6a) and reported the number of true positives (TP) and false positives (FP) achieved by both crowdsourcing methods for each type of task. Figure 6b depicts these results. The most notorious result in this task is the assessment performance for the datatype “number”. The experts effectively identified triples where the datatype was incorrectly assigned as “number” 13 for instance, in the triple where the crowd was confused and determined that datatype was correct, thus generating a large number of false positives. Nevertheless, it could be argued that the data type “number” in the previous example is not completely incorrect, when being unaware of the fact that there are more specific data types for representing time units. Under this assumption, the precision of the crowd would have been 0.8475 and 0.8211 for first answer and majority voting, respectively." ?g. }
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- paragraph hasContent "In order to understand the previous results, we analyzed the performance of experts and workers at a more fine-grained level. We calculated the frequency of occurrences of datatypes in the assessed triples (see Figure 6a) and reported the number of true positives (TP) and false positives (FP) achieved by both crowdsourcing methods for each type of task. Figure 6b depicts these results. The most notorious result in this task is the assessment performance for the datatype “number”. The experts effectively identified triples where the datatype was incorrectly assigned as “number” 13 for instance, in the triple where the crowd was confused and determined that datatype was correct, thus generating a large number of false positives. Nevertheless, it could be argued that the data type “number” in the previous example is not completely incorrect, when being unaware of the fact that there are more specific data types for representing time units. Under this assumption, the precision of the crowd would have been 0.8475 and 0.8211 for first answer and majority voting, respectively." assertion.