E-learning, Mock data, Training materials, Video
Advanced SPARQL queries and best practices
This training was developed in the context of the Swiss Personalized Health Network (SPHN) initiative and is part of a series of trainings centred around the SPHN Semantic Interoperability Framework developed by the SPHN Data Coordination Center (DCC). The framework aims at facilitating collaborative research by providing a decentralized infrastructure sustained by a strong semantic layer (SPHN Dataset) and graph technology, based on RDF, for the exchange and storage of data.
This training is a follow up to the Querying Data with SPARQL training and provides examples of more advanced SPARQL queries (e.g. negation, property paths, inference, named graph), based on the SPHN RDF Schema 2024.2. The training also covers best practices to apply when building a SPARQL query. You will also learn about some hints that may help you in fixing or refining a query you have written for more efficiency.
Resources:
All resources are available on the training's GitLab space
Licence: Creative Commons Attribution Non Commercial Share Alike 4.0 International
Keywords: Clinical data, Inference, Knowledge graph, Ontology, Query data, RDF, SPARQL, Triplestore
Target audience: Research Scientists, Data Managers, Bioinformaticians, Biomedical Researchers, Data Scientists
Resource type: E-learning, Mock data, Training materials, Video
Status: Active
Prerequisites:
This video assumes that you have basic knowledge about SPARQL and that you have watched the “Querying Data with SPARQL” training video (see link in the description).
Learning objectives:
After the training you will be able to:
- Generate complex SPARQL queries
- Refine your SPARQL query for better efficiency
- Identify possible solutions when your SPARQL query do not run as expected
- Be critical about results obtained from a SPARQL query
Scientific topics: Computer science, Data management, FAIR data, Medical informatics
Activity log
