2015/01/08 by Mayank Kejriwal, Kejriwal, Mayank, Daniel P. Miranker +1 · 1 citation
Computer Science · Decision Sciences · #Anomaly Detection Techniques and Applications #Data Quality and Management #Databases (cs.DB) #Digital and Cyber Forensics #FOS: Computer and information sciences #cs.DB
paper · pdf · doi:10.48550/arxiv.1501.01694
arxiv created 2015/01/08 · openalex publication_date 2015/01/08 · arxiv updated 2015/01/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Entity Resolution concerns identifying co-referent entity pairs across datasets. A typical workflow comprises two steps. In the first step, a blocking method uses a one-many function called a blocking scheme to map entities to blocks. In the second step, entities sharing a block are paired and compared. Current DNF blocking scheme learners (DNF-BSLs) apply only to structurally homogeneous tables. We present an unsupervised algorithmic pipeline for learning DNF blocking schemes on RDF graph datasets, as well as structurally heterogeneous tables. Previous DNF-BSLs are admitted as special cases. We evaluate the pipeline on six real-world dataset pairs. Unsupervised results are shown to be competitive with supervised and semi-supervised baselines. To the best of our knowledge, this is the first unsupervised DNF-BSL that admits RDF graphs and structurally heterogeneous tables as inputs.