The Battleship Approach to the Low Resource Entity Matching Problem
Bar Genossar Avigdor Gal Roee Shraga
Entity matching, a core data integration problem, is the task of deciding
whether two data tuples refer to the same real-world entity. Recent advances in
deep learning methods, using pre-trained language models, were proposed for
resolving entity matching. Although demonstrating unprecedented results, these
solutions suffer from a major drawback as they require large amounts of labeled
data for training, and, as such, are inadequate to be applied to low resource
entity matching problems. To overcome the challenge of obtaining sufficient
labeled data we offer a new active learning approach, focusing on a selection
mechanism that exploits unique properties of entity matching. We argue that a
distributed representation of a tuple pair indicates its informativeness when
considered among other pairs. This is used consequently in our approach that
iteratively utilizes space-aware considerations. Bringing it all together, we
treat the low resource entity matching problem as a Battleship game, hunting
indicative samples, focusing on positive ones, through awareness of the latent
space along with careful planning of next sampling iterations. An extensive
experimental analysis shows that the proposed algorithm outperforms
state-of-the-art active learning solutions to low resource entity matching, and
although using less samples, can be as successful as state-of-the-art fully
trained known algorithms.