Triple stores and data storage certification by Markus Stocker

Описание: Triple stores constitute a popular database technology for linked graph data and were developed to manage (store, query, inference) the growing amount of Resource Description Framework (RDF) data.
RDF data is a set of statements, each of them being a triple consisting of a subject (URI), a predicate (URI) and an object (URI, Literal). They are part of the so-called semantic web technology stack.
Databases for RDF data are examples of triple stores (e.g. Apache Jena/Fuseki, Virtuoso, Stardog, Allegro Graph, D2RQ). What is included in the database can be represented in various formats, depending on the human or machine requesting the content. There are some common languages used to express queries across the Web (ontology languages - RDFs, OWL, SPARQL query language). Triple stores are a relevant technology to FAIR data in particular because of the Principles’ reference to languages for knowledge representation (Interoperability). In state-of-the-art information systems, the Web Ontology Language (OWL) is the standard language for knowledge representation. OWL ontologies are encoded as RDF. Hence, triple stores can be used to manage OWL ontologies as well as
data that instantiate OWL ontologies. With their support for formal (i.e. machine-readable) semantics and inference, semantic web technologies are also relevant to (semantic) findability of data as well as (machine) re-usability.

In this video, Marku Stocker summarizes the work of the ENVRI-FAIR working group that is assigned to the investigation of triple stores and their role in
the ENVRI community and the ENVRI-FAIR project.

Visit our website for more information about the ENVRI community and the collaborating research infrastructures:
www.envri.eu

Presenter: Maggie Hellström
Event: The ENVRI-Hub for EOSC and Planet Earth event at the RDA 17th plenary

ENVRI community collaboration is supported by the ENVRI-FAIR project. The project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 824068.