Applications consuming data have to deal with variety of data quality issues such as missing values, duplication, incorrect values, etc. Although automatic approaches can be utilized for data cleaning the results can remain uncertain. Therefore updates suggested by automatic data cleaning algorithms require further human verification. This paper presents an approach for generating tasks for uncertain updates and routing these tasks to appropriate workers based on their expertise. Specifically the paper tackles the problem of modelling the expertise of knowledge workers for the purpose of routing tasks within collaborative data quality management. The proposed expertise model represents the profile of a worker against a set of concepts describing the data. A simple routing algorithm is employed for leveraging the expertise profiles for matching data cleaning tasks with workers. The proposed approach is evaluated on a real world dataset using human workers. The results demonstrate the effectiveness of using concepts for modelling expertise, in terms of likelihood of receiving responses to tasks routed to workers.