Distributed Holistic Clustering on Linked Data

TL;DR

This paper introduces a scalable distributed clustering method for link discovery across multiple linked data sources, improving efficiency and accuracy in identifying entity matches and errors in large datasets.

Contribution

It presents a novel distributed holistic clustering approach for multi-source link discovery, addressing scalability and effectiveness issues in large Web of Data datasets.

Findings

Effective in large geographic and music datasets

Improves link discovery accuracy and error detection

Achieves faster execution times through distributed processing

Abstract

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.