  We propose a new approach for modeling and reconciling conflicting data
cleaning actions. Such conflicts arise naturally in collaborative data curation
settings where multiple experts work independently and then aim to put their
efforts together to improve and accelerate data cleaning. The key idea of our
approach is to model conflicting updates as a formal \emph{argumentation
framework}(AF). Such argumentation frameworks can be automatically analyzed and
solved by translating them to a logic program $P_{AF}$ whose declarative
semantics yield a transparent solution with many desirable properties, e.g.,
uncontroversial updates are accepted, unjustified ones are rejected, and the
remaining ambiguities are exposed and presented to users for further analysis.
After motivating the problem, we introduce our approach and illustrate it with
a detailed running example introducing both well-founded and stable semantics
to help understand the AF solutions. We have begun to develop open source tools
and Jupyter notebooks that demonstrate the practicality of our approach. In
future work we plan to develop a toolkit for conflict resolution that can be
used in conjunction with OpenRefine, a popular interactive data cleaning tool.
