About us

The goal of this workshop is to bring together researchers, practitioners and experts in entity resolution and graph alignment to introduce new methods and techniques, discuss open challenges, share benchmarks and explore future research directions across relational, graph-based and Linked Data environments.

Important Dates

Fri, Jun 12, 2026

Paper submission

Fri, Jun 26, 2026

Author notification

Fri, Jul 3, 2026

Camera-ready submission

Mon, Sep 28, 2026

Workshop

Paper Submission

Research Paper Submission Tracks

Data fusion

Instance matching

Blocking and filtering for entity resolution

Named entity resolution

Collective entity resolution

Knowledge graph entity alignment

Knowledge graph completion and consolidation

LLM-assisted entity linking and graph merging

Ontology matching

Identity linking in Linked Data

Explainable AI for identity resolution

Cross-lingual entity alignment

Learning based approaches (supervised and unsupervised methods) to entity resolution and graph alignment

Representation learning for graph alignment

Deep learning based approaches for graph alignment

Crowdsourcing for entity resolution and graph alignment

Dynamic graph entity alignment

Streaming entity resolution

Entity resolution for big data

Parallel entity resolution

Privacy-preserving entity resolution and graph alignment

Fairness in entity resolution

Entity resolution for geospatial data

Empirical comparison of entity resolution methods

Benchmarks for entity resolution and graph alignment

Tools and platforms

Linked Data management

Call for Papers Description

The workshop is calling for original LONG (up to 16 LNCS style pages) and SHORT (up to 10 LNCS style pages) papers that have not been published elsewhere.