CollaborEM: A self-supervised entity matching framework using multi-features collaboration

Publication Type

Journal Article

Publication Date

12-2023

Abstract

Entity Matching (EM) aims to identify whether two tuples refer to the same real-world entity and is well-known to be labor-intensive. It is a prerequisite to anomaly detection, as comparing the attribute values of two matched tuples from two different datasets provides one effective way to detect anomalies. Existing EM approaches, due to insufficient feature discovery or error-prone inherent characteristics, are not able to achieve stable performance. In this paper, we present CollaborEM, a self-supervised entity matching framework via multi-features collaboration. It is capable of (i) obtaining reliable EM results with zero human annotations and (ii) discovering adequate tuples’ features in a fault-tolerant manner. CollaborEM consists of two phases, i.e., automatic label generation (ALG) and collaborative EM training (CEMT). In the first phase, ALG is proposed to generate a set of positive tuple pairs and a set of negative tuple pairs. ALG guarantees the high quality of the generated tuples, and hence ensures the training quality of the subsequent CEMT. In the second phase, CEMT is introduced to learn the matching signals by discovering graph features and sentence features of tuples collaboratively. Extensive experimental results over eight real-world EM benchmarks show that CollaborEM outperforms all the existing unsupervised EM approaches and is comparable or even superior to the state-of-the-art supervised EM methods.

Keywords

Entity matching, sentence feature, graph feature, self-supervised, anomaly detection

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

IEEE Transactions on Knowledge and Data Engineering

Volume

35

Issue

12

First Page

12139

Last Page

12152

ISSN

1041-4347

Identifier

10.1109/TKDE.2021.3134806

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

https://doi.org/10.1109/TKDE.2021.3134806

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