  A challenge for data imputation is the lack of knowledge. In this paper, we
attempt to address this challenge by involving extra knowledge from web. To
achieve high-performance web-based imputation, we use the dependency, i.e.FDs
and CFDs, to impute as many as possible values automatically and fill in the
other missing values with the minimal access of web, whose cost is relatively
large. To make sufficient use of dependencies, We model the dependency set on
the data as a graph and perform automatical imputation and keywords generation
for web-based imputation based on such graph model. With the generated
keywords, we design two algorithms to extract values for imputation from the
search results. Extensive experimental results based on real-world data
collections show that the proposed approach could impute missing values
efficiently and effectively compared to existing approach.
