Distributed representations of tuples for entity resolution

Abstract

Despite the efforts in 70+ years in all aspects of entity resolution (ER), there is still a high demand for democratizing ER - by reducing the heavy human involvement in labeling 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 system, called D eep ER, that achieves good accuracy, high efficiency, 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 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 that are customized for a specific ER task under different scenarios. We propose a locality sensitive hashing (LSH) based blocking approach that takes all attributes of a tuple into consideration and produces much smaller blocks, compared with traditional methods that consider 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 D eep ER outperforms existing solutions.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 88 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.