# VoIDext: Vocabulary and Patterns for Enhancing Interoperable Datasets with Virtual Links

## TL;DR

This paper introduces VoIDext, an extension of the Vocabulary of Interlinked Datasets, adding concepts like virtual links and data patterns to improve semantic interoperability and ease querying across heterogeneous RDF datasets.

## Contribution

It proposes new terms and data model patterns for VoID to facilitate linking and understanding diverse RDF datasets, addressing semantic heterogeneity and interoperability challenges.

## Key findings

- Enhanced dataset linking with virtual links improves interoperability.
- Application to real-world RDF datasets demonstrates practical benefits.
- Virtual links facilitate more efficient data querying and integration.

## Abstract

Semantic heterogeneity remains a problem when interoperating with data from sources of different scopes and knowledge domains. Causes for this challenge are context-specific requirements (i.e. no "one model fits all"), different data modelling decisions, domain-specific purposes, and technical constraints. Moreover, even if the problem of semantic heterogeneity among different RDF publishers and knowledge domains is solved, querying and accessing the data of distributed RDF datasets on the Web is not straightforward. This is because of the complex and fastidious process needed to understand how these datasets can be related or linked, and consequently, queried. To address this issue, we propose to extend the existing Vocabulary of Interlinked Datasets (VoID) by introducing new terms such as the Virtual Link Set concept and data model patterns. A virtual link is a connection between resources such as literals and IRIs (Internationalized Resource Identifier) with some commonality where each of these resources is from a different RDF dataset. The links are required in order to understand how to semantically relate datasets. In addition, we describe several benefits of using virtual links to improve interoperability between heterogenous and independent datasets. Finally, we exemplify and apply our approach to multiple world-wide used RDF datasets.