Unsupervised Entity Resolution With Blocking and Graph Algorithms

Entity resolution identifies all records in a database that refer to the same entity. In this paper, we propose an unsupervised framework for entity resolution using blocking and graph algorithms. The records are partitioned into blocks with no redundancy for efficiency improvement. For intra-block...

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Veröffentlicht in:IEEE transactions on knowledge and data engineering Jg. 34; H. 3; S. 1501 - 1515
Hauptverfasser: Zhang, Dongxiang, Li, Dongsheng, Guo, Long, Tan, Kian-Lee
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.03.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Bipartite graph
Blocking
Crowdsourcing
Data processing
Deep learning
Graph theory
graph-based algorithm
Machine learning
Measurement
Nuclear power plants
Redundancy
Task analysis
Training
Unsupervised entity resolution
ISSN:1041-4347, 1558-2191
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Zusammenfassung:Entity resolution identifies all records in a database that refer to the same entity. In this paper, we propose an unsupervised framework for entity resolution using blocking and graph algorithms. The records are partitioned into blocks with no redundancy for efficiency improvement. For intra-block data processing, we propose a graph-theoretic fusion framework with two components, namely ITER and CliqueRank. Specifically, ITER constructs a weighted bipartite graph between terms and record-record pairs and iteratively propagates the node salience until convergence. Subsequently, CliqueRank constructs a record graph to estimate the likelihood of two records resident in the same clique. The derived likelihood from CliqueRank is fed back to ITER to rectify the edge weight until a joint optimum can be reached. Experimental evaluation was conducted with 4 real datasets. Results show that our unsupervised framework is comparable or even superior to state-of-the-art deep learning approaches.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2020.2991063