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Towards One-Size-Fits-Many: Multi-Context Attention Network for Diversity of Entity Resolution Tasks.

Authors :
Zhang, Dongxiang
Li, Zepeng
Wang, Xiaoli
Tan, Kian-Lee
Chen, Gang
Source :
IEEE Transactions on Knowledge & Data Engineering; Dec2022, Vol. 34 Issue 12, p6018-6032, 15p
Publication Year :
2022

Abstract

Entity resolution (ER) identifies data instances referring to the same real-world entity and has received enormous research attention. In this paper, we examine the task of ER from a broader perspective, with its input extended from textual records, which are conventionally studied in the literature, to other modalities such as check-in sequences, GPS trajectories and surveillance video frames to generate new applications. Our goal in this paper is to design an effective model to uniformly support all these ER applications with different input formats. Technically, we fully exploit the semantic contexts of embedding vectors for the pair of input instances. In particular, we propose an integrated multi-context attention framework that takes into account self-attention, pair-attention and global-attention from three types of context. The idea can be further extended to incorporate attribute attention in order to support structured datasets. We conduct extensive experiments on a diverse class of entity resolutions tasks, including tasks on unstructured, structured and dirty textual records, check-in sequences, GPS trajectories and surveillance video frames. The experimental results verified the effectiveness and generality of our model. When compared with strong baselines in these applications, our model can achieve superior or comparative performance. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10414347
Volume :
34
Issue :
12
Database :
Complementary Index
Journal :
IEEE Transactions on Knowledge & Data Engineering
Publication Type :
Academic Journal
Accession number :
160692081
Full Text :
https://doi.org/10.1109/TKDE.2021.3060790