Merging datasets through deep learning

TL;DR

This paper introduces a deep learning-based method for merging datasets by recognizing different surface forms of the same entity, improving over traditional string-based methods through vector space modeling and nearest neighbor search.

Contribution

The paper presents a novel deep learning approach with specialized metric learning techniques for entity surface form matching, enabling more accurate dataset merging.

Findings

Achieved precision@1 of 0.75-0.81 in entity matching

Achieved recall of 0.74-0.81 in entity matching

Models are publicly available for use in dataset alignment

Abstract

Merging datasets is a key operation for data analytics. A frequent requirement for merging is joining across columns that have different surface forms for the same entity (e.g., the name of a person might be represented as "Douglas Adams" or "Adams, Douglas"). Similarly, ontology alignment can require recognizing distinct surface forms of the same entity, especially when ontologies are independently developed. However, data management systems are currently limited to performing merges based on string equality, or at best using string similarity. We propose an approach to performing merges based on deep learning models. Our approach depends on (a) creating a deep learning model that maps surface forms of an entity into a set of vectors such that alternate forms for the same entity are closest in vector space, (b) indexing these vectors using a nearest neighbors algorithm to find the forms that can be potentially joined together. To build these models, we had to adapt techniques from metric learning due to the characteristics of the data; specifically we describe novel sample selection techniques and loss functions that work for this problem. To evaluate our approach, we used Wikidata as ground truth and built models from datasets with approximately 1.1M people's names (200K identities) and 130K company names (70K identities). We developed models that allow for joins with precision@1 of .75-.81 and recall of .74-.81. We make the models available for aligning people or companies across multiple datasets.