  Entity resolution (ER) is a key data integration problem. Despite the efforts
in 70+ years in all aspects of ER, there is still a high demand for
democratizing ER - humans are heavily involved in labeling data, performing
feature engineering, tuning parameters, and defining blocking functions. With
the recent advances in deep learning, in particular distributed representation
of words (a.k.a. word embeddings), we present a novel ER system, called DeepER,
that achieves good accuracy, high efficiency, as well as ease-of-use (i.e.,
much less human efforts). For accuracy, we use sophisticated composition
methods, namely uni- and bi-directional recurrent neural networks (RNNs) with
long short term memory (LSTM) hidden units, to convert each tuple to a
distributed representation (i.e., a vector), which can in turn be used to
effectively capture similarities between tuples. We consider both the case
where pre-trained word embeddings are available as well the case where they are
not; we present ways to learn and tune the distributed representations. For
efficiency, we propose a locality sensitive hashing (LSH) based blocking
approach that uses distributed representations of tuples; it takes all
attributes of a tuple into consideration and produces much smaller blocks,
compared with traditional methods that consider only a few attributes. For
ease-of-use, DeepER requires much less human labeled data and does not need
feature engineering, compared with traditional machine learning based
approaches which require handcrafted features, and similarity functions along
with their associated thresholds. We evaluate our algorithms on multiple
datasets (including benchmarks, biomedical data, as well as multi-lingual data)
and the extensive experimental results show that DeepER outperforms existing
solutions.
