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

• Main Authors: GE, Congcong, WANG, Pengfei, CHEN, Lu, LIU, Xiaoze, ZHENG, Baihua, GAO, Yunjun
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Entity matching; sentence feature; graph feature; self-supervised; anomaly detection; Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/8341
• Institution: Singapore Management University

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 an...