YARRRML + LDES : simultaneously lowering complexity from knowledge graph generation and publication

Abstract

Linked Data Event Streams (LDES) is an advanced Knowledge Graph (KG) publication specification aimed at continuous data source replication and synchronization with benefits such as data entities versioning and history retention while providing a self-descriptive API. However, building an LDES requires a high level of expertise in the Semantic Web ecosystem. In this demo paper, we show how we lower the complexity and need for expertise when using a more advanced KG publication method such as LDES by providing an extension point to YARRRML, a human-friendly way to configure KG generation via RML. Integrated in Matey, an online YARRRML editor, we show how little effort (adding five characters to the YARRRML syntax in the simplest case) allows (re)generating multiple versions of a KG as an Event Stream. As such, this extension provides an easy-to-use starting point for anyone wanting to

create an LDES from non-semantic data.