# Fine-Grained Tasks for Crowdsourced Entity Resolution
## 7 Experiments
### Quality vs. Cost Experiments  
In this subsection, the results of comparing our method with the other two methods are presented and analyzed. **Quality. ** A comparison of their recall performance is shown in Figure 7(a). For CrowdER, the main factor affecting the recall is that the pruning operation resolves the record pairs with a low degree of similarity but actually matches mismatched pairs directly. When POWER uses the partial order relationship for speculation, some record pairs that are ranked before some actually matching pairs in the partial order relationship are marked as mismatched, resulting in this part of records being determined as mismatched. The main factor affecting the recall of the FGTER method proposed in this paper is that the matched records are divided into different record blocks, which are more fault-tolerant because the correlation on each attribute is taken into account. Figure 7(b) shows the experimental results for accuracy, and FGTER significantly outperforms the other two methods in terms of accuracy. The same factors affecting the recall also affect the accuracy. CrowdER leads to mislabeling due to pruning operation, POWER's speculation on the partial order  
Figure 7: Entity status in datasets. relationship leads to mislabeling of mismatched data, and FGTER, with its record block splitting, appears to mark mismatched data as matching less often, mainly because the initial record block splitting stage clusters the attributes in the same cluster. These records happen to match on other attributes, and the probability of this occurring is significantly lower compared to the other two methods. It can be seen that using attribute correlation can improve the overall fault tolerance of the experiment. Finally, the combined precision and recall are calculated for the F-1 values in the experimental situation in Figure (c)c. **Cost. ** Another key evaluation metric is the cost. Crowder and POWER use pairwise matching tasks, while the method in this paper uses attribute-based fine-grained tasks with lower single-task cost. The number of tasks is shown in Figure 9.  
From the figure, we can see that FGTER is higher than POWER in terms of the number of tasks, although FGTER requires only one attribute matching task for data block splitting. However, it needs to judge all the attribute clusters in the record block in the process of resolving the record block into a complete block, which is also a bottleneck for this algorithm. Overall, considering the experimental cost, its performance is poor on the Restaurant dataset, mainly due to a large number of mismatched entities in the dataset, and the splitting effect of the data is not good due to some attribute values being shared by multiple entities. FGTER performs better on the Cora dataset, as there are more duplicate entities in Cora, the attribute clustering effect is better, the record block division is more obvious, and data records pointing to the same entity are more easily divided into the same record block, reducing a large number of attribute matching tasks. There are two main reasons for the wrong record block division by the method proposed in this paper. One reason is that some data records have a poor correlation with  
Figure 8: Quality comparison experiment. Figure 9: Cost comparison experiment. each attribute, resulting in them not being divided into the same record block. The other reason is that some matching records can determine the matching relationship only on that attribute when the record block is split according to the attribute scoring task, resulting in some matching records being split into different record blocks. The attribute matching task and the attribute scoring task proposed in this paper have a smaller granularity compared to the pairwise record matching task, which is equivalent to splitting a pairwise matching task according to the number of attributes.