  In this paper, for the first time, we introduce the concept of skyblocking,
which aims to efficiently identify the "most preferred" blocking scheme in
terms of a given set of selection criteria for entity resolution blocking. To
capture all possible preferred blocking schemes, scheme skyline (i.e. blocking
schemes on the skyline) has been studied in a multi-dimensional scheme space
with dimensions corresponding to selection criteria for blocking (e.g. PC and
PQ). However, applying traditional skyline techniques to learn scheme skylines
is a non-trivial task. Due to the unique characteristics of blocking schemes,
we face several challenges, such as: how to find a balanced number of match and
non-match labels to effectively approximate a block scheme in a scheme space,
and how to design efficient skyline algorithms to explore a scheme space for
finding scheme skylines. To overcome these challenges, we propose a scheme
skyline learning approach, which incorporates skyline techniques into an active
learning process of scheme skylines. We have conducted experiments over four
real-world datasets. The experimental results show that our approach is able to
efficiently identify scheme skylines in a large scheme space only using a
limited number of labels. Our approach also outperforms the state-of-the-art
approaches for learning blocking schemes in several aspects, including: label
efficiency, blocking quality and learning efficiency.
