# Fine-Grained Tasks for Crowdsourced Entity Resolution  
[PERSON] School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China; [EMAIL_ADDRESS]. needs of different people, while active-learning-based entity resolution is more suitable for fulfilling large scale requirements. With a large number of data objects in a dirty dataset, there may be a situation in which multiple data objects describe the same entity, and if two records describe the same entity, then they are duplicated and redundant. Table 1 shows some data details in the Restaurant dataset. Two pairs of records \((r_{1},r_{2}),\ (r_{3},r_{4})\) are matched, while other pairs of records such as \((r_{1},r_{3}),\ (r_{3},r_{2})\) are not matched, and finally, we obtained the following result: \(C=\left\{C_{1}=(r_{1},r_{2}),\ C_{2}=(r_{3},r_{4})\right\}\). Using the crowdsourcing platform, we can obtain some information that is not available from the machine, such as the matching relationship of two indistinguishable record pairs. The existing methods usually need to obtain the matching relationships between pairs of records [12; 13; 14; 15] and use these for truth inference to achieve the purpose of parsing the whole dataset [12; 16; 17; 18; 19]. In a database, large amounts of structured data are stored, which consist of different attributes, and obtaining the matching relationships between attributes can also achieve the purpose of entity resolution. In the record pair matching task, we cannot obtain the exact matching relationship between attributes, ignoring the information carried by the attributes in data records. Based on the above analysis, this paper proposes a crowdsourced entity resolution method that focuses on the relationship between attributes within the data, models the entity resolution problem as a data partitioning problem, reduces the granularity of manual tasks to specific attributes, proposes two kinds of fine-grained tasks, plays a role in reducing costs, and shows an improvement in handling the possible false-positive situation in which there is a high similarity between record pairs that do not actually match. The first challenge we face is to choose which attributes to use for the matching task. For a dataset containing n data, which contains \(n*\frac{n-1}{2}\) pairs of records, each with m attributes, there are \(m*n*\frac{n-1}{2}\) pairs of attributes to be matched, and we aim to minimize the number of manual tasks, so the fewer attribute pairs to be matched, the better. The second challenge is handling a dataset in which multiple pieces of data share the same attribute with different relationships between them. The third challenge is making reasonable use of the attribute relationships between records to complete the task of resolving the data. The main contributions of this paper are as follows:  
* Crowdsourced entity resolution with fine-grained tasks is proposed to perform entity resolution within a record block by merging and splitting attributes;
* We propose an attribute pair matching task to obtain the information association relationship within a record pair and correctly match the attributes for the purpose of parsing entities;
* The attribute scoring task is proposed to assist the attribute matching task to solve the problem of sharing attributes within a record by scoring a certain type of attribute;
* For the task generation method, a density-based task selection order and a benefit-state-based task selection method are proposed to utilize each task as much as possible for the purpose of cost control; \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Id** & **Name** & **Address** & **City** & **Type** \\ \hline \(r_{1}\) & Art’s deli & 12224 Ventura blvd. & Studio City & Delis \\ \(r_{2}\) & Arts’ delicatessen & 12224 Ventura blvd. & Studio City & American \\ \(r_{3}\) & Hotel Bel-air & 701 Stone Canyon rd. & Bel air & California \\ \(r_{4}\) & Bel-air Hotel & 701 Stone Canyon rd. & Bel air & California \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Restaurant data fragments.