# Distributed Holistic Clustering on Linked Data

arXiv: https://arxiv.org/abs/1708.09299 · HTML: https://mlsanity.com/paper/1708.09299/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2017.

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

- [Graph-based Active Learning for Entity Cluster Repair](https://mlsanity.com/paper/2401.14992/) (2024)

## Abstract (arXiv)

Link discovery is an active field of research to support data integration in the Web of Data. Due to the huge size and number of available data sources, efficient and effective link discovery is a very challenging task. Common pairwise link discovery approaches do not scale to many sources with very large entity sets. We here propose a distributed holistic approach to link many data sources based on a clustering of entities that represent the same real-world object. Our clustering approach provides a compact and fused representation of entities, and can identify errors in existing links as well as many new links. We support a distributed execution of the clustering approach to achieve faster execution times and scalability for large real-world data sets. We provide a novel gold standard for multi-source clustering, and evaluate our methods with respect to effectiveness and efficiency for large data sets from the geographic and music domains.