# A DNF Blocking Scheme Learner for Heterogeneous Datasets

arXiv: https://arxiv.org/abs/1501.01694 · HTML: https://mlsanity.com/paper/1501.01694/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2015.

## Built on by (most cited first, 1 of 1)

- [Named Entity Resolution in Personal Knowledge Graphs](https://mlsanity.com/paper/2307.12173/) (2023)

## Abstract (arXiv)

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.