Despite the efforts in 70+ years in all aspects of Entity res- olution (ER), there is still a high demand for democratizing ER – by reducing the heavy human involvement in label- ing data, performing feature engineering, tuning parameters, and defining blocking functions. With the recent advances in deep learning, in particular distributed representations of words (a.k.a. word embeddings), we present a novel ER sys- tem, called DeepER, that achieves good accuracy, high effi- ciency, as well as ease-of-use (i.e., much less human efforts). 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 be- tween 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 that are customized for a specific ER task under different scenarios. We propose a locality sensitive hashing (LSH) based blocking approach that takes all at- tributes of a tuple into consideration and produces much smaller blocks, compared with traditional methods that con- sider only a few attributes. 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.

@inproceedings{Ebraheem-et-al-pvldb-18, abstract = {Despite the efforts in 70+ years in all aspects of Entity res- olution (ER), there is still a high demand for democratizing ER – by reducing the heavy human involvement in label- ing data, performing feature engineering, tuning parameters, and defining blocking functions. With the recent advances in deep learning, in particular distributed representations of words (a.k.a. word embeddings), we present a novel ER sys- tem, called DeepER, that achieves good accuracy, high effi- ciency, as well as ease-of-use (i.e., much less human efforts). 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 be- tween 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 that are customized for a specific ER task under different scenarios. We propose a locality sensitive hashing (LSH) based blocking approach that takes all at- tributes of a tuple into consideration and produces much smaller blocks, compared with traditional methods that con- sider only a few attributes. 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.}, address = {Rio de Janeiro, Brazil}, author = {Muhammad Ebraheem and Saravanan Thirumuruganathan and Shafiq Joty and Mourad Ouzzani and Nan Tang}, booktitle = {The Forty-fourth International Conference on Very Large Data Bases}, month = {August}, number = {10}, pages = {1454 -- 1467}, publisher = {}, series = {VLDB-2018}, title = {Distributed Representations of Tuples for Entity Resolution}, url = {http://www.vldb.org/pvldb/vol11/p1454-ebraheem.pdf}, volume = {11}, year = {2018} }