A critical reality in data integration is that knowledge from different sources may often be conflicting with each other. Conflict resolutioncan be costly and, if done without proper context, can be ineffective. In this paper, we propose a novel query-driven and feedback-based approach (FICSR1) to conflict resolution when integrating data sources. In particular, instead of relying on traditional model based definition of consistency, we introduce a ranked interpretation. This not only enables FICSR to deal with the complexity of the conflict resolution process, but also helps achieve a more direct match between the users' (subjective) interpretation of the data and the system's (objective) treatment of the available alternatives. Consequently, the ranked interpretation leads to new opportunities for bi-directional (data informsover ↔ user) feedback cycle for conflict resolution: given a query, (a) a preliminary ranking of candidate results on data can inform the user regarding constraints critical to the query, while (b) user feedback regarding the ranks can be exploited to inform the system about user's relevant domain knowledge. To enable this feedback process, we develop data structures and algorithms for efficient off-line conflict/agreement analysis of the integrated data as well as for on-line query processing, candidate result enumeration, and validity analysis. The results are brought together and evaluated in the FICSR system.