TY - JOUR
T1 - Entity resolution with weighted constraints
AU - Shen, Zeyu
AU - Wang, Qing
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2014.
PY - 2014
Y1 - 2014
N2 - Constraints ubiquitously exist in many real-life applications for entity resolution (ER). However, it is always challenging to effectively specify and efficiently use constraints when performing ER tasks. In particular, not every constraint is equally effective or robust, and using weights to express the “confidences” on constraints becomes a natural choice. In this paper, we study entity resolution (ER) (i.e., the problem of determining which records in a database refer to the same entities) in the presence of weighted constraints. We propose a unified framework that can interweave positive and negative constraints into the ER process, and investigate how effectively and efficiently weighted constraints can be used for generating ER clustering results. Our experimental study shows that using weighted constraints can lead to improved ER quality and scalability.
AB - Constraints ubiquitously exist in many real-life applications for entity resolution (ER). However, it is always challenging to effectively specify and efficiently use constraints when performing ER tasks. In particular, not every constraint is equally effective or robust, and using weights to express the “confidences” on constraints becomes a natural choice. In this paper, we study entity resolution (ER) (i.e., the problem of determining which records in a database refer to the same entities) in the presence of weighted constraints. We propose a unified framework that can interweave positive and negative constraints into the ER process, and investigate how effectively and efficiently weighted constraints can be used for generating ER clustering results. Our experimental study shows that using weighted constraints can lead to improved ER quality and scalability.
UR - http://www.scopus.com/inward/record.url?scp=84921656286&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-10933-6_23
DO - 10.1007/978-3-319-10933-6_23
M3 - Article
SN - 0302-9743
VL - 8716
SP - 308
EP - 322
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -