SPHN Semantic Framework

SPHN framework

• Introduction
  • The Data challenge
  • SPHN Semantic Interoperability Strategy
  • The Stakeholder Roles
  • SPHN Data Ecosystem for FAIR Data
  • Reference
• SPHN Dataset
  • Introduction
  • Scope of the SPHN Dataset
  • Guiding principles for concept design
  • Specification of a concept
  • Development process
  • Availability and usage rights
• SPHN RDF Schema
  • Scope of the SPHN RDF Schema
  • Technical specification of the schema
  • Development process
  • Availability and usage rights
• SHACLer
  • SHACL
  • The SHACLer tool
  • SHACLer internals
  • Validating data with the SHACL file
  • Availability and usage rights
• SPARQLer
  • SPARQL
  • The SPARQLer tool
  • Available SPARQL Queries
  • Availability and usage rights
• Quality Assurance Framework
  • The SPHN RDF Quality Check Tool
  • Statistical SPARQL queries
  • Data validation with SHACL
  • Availability and usage rights
• Terminology Service
  • External terminologies
  • The SPHN Terminology Service
  • Downloading terminology files
  • Reference

External resources used

• External terminologies
• Naming conventions
• ATC
• CHOP
• GENO
• HGNC
• ICD-10-GM
• LOINC
• SNOMED CT
• SO
• UCUM
• Other terminologies

User guide - How to?

• Access, read and use of SPHN content
  • Access the SPHN Dataset and the RDF Schema
  • Read and use of the SPHN Dataset
  • Read and use of the SPHN RDF Schema
• Generate a project RDF Schema from the the RDF Schema Template
  • Target Audience
  • 1. Project-specific schema creation
  • 2. New concepts and modification of existing ones
  • 3. Valuesets as individuals in the RDF schema
  • 4. Best practices when generating the RDF
  • 5. Visualizing the project-specific schema
  • 6. Validating the project-specific schema
  • 7. Reporting back to DCC
• Generate a project RDF Schema from the Dataset Template
  • Target audience
  • Introduction
  • 1. Add project metadata
  • 2. Concept definition
  • 3. Add a composedOf (attribute) to an existing SPHN concept
  • 4. Transform the Dataset Template to a RDF Schema
• Instantiate data according to the project RDF Schema
  • Target Audience
  • 1. Versioning of the data
  • 2. Instantiation of the data
  • 3. Cardinality of the data
• Improve data quality through validation
  • Target Audience
  • 1. Data quality validation using the RDF Quality Check Tool
  • 2. SHACL validation in GraphDB
• SHACL constraints in the SHACLer
  • Target Audience
  • 1. Assumptions
  • 2. SHACL constraints implemented for SPHN
  • 3. Template of SHACL constraints
  • 4. Implementation examples
• Visually explore data with GraphDB
  • Target Audience
  • Loading data in the GraphDB triplestore
  • Schema and data visualization
  • Availability
• Query data with SPARQL
  • Target Audience
  • Data profiling querying - Summary statistics
  • Data exploration querying
  • Setup inference in GraphDB
  • References
• Concept Flattening in the SPARQLer
  • Target Audience
  • Definitions
  • Concept flattening
  • Statistical queries
• Use Python and R with RDF data
  • Target Audience
  • Overview
  • Loading data from GraphDB in Python and R
  • Combining results from different queries
  • Further Information
• Download external terminologies from the Terminology Service
  • Target Audience
  • Downloading external terminologies from the BioMedIT portal
  • Downloading external terminologies from the Terminology Server
• FAIRification of External Terminologies in RDF for projects
  • Target audience
  • Identify the terminology of interest
  • Define the set of metadata of interest
  • Translate terminology content to RDF elements
  • Hands on example

Examples of Implementation

• CHUV with Oracle
  • Introduction
  • Implementation
  • Example of use
• SPHN export application at USB
  • Introduction
  • Application Overview
  • USB export application: Python Program
  • Examples of SQL view statement for SPHN transformation layer
• USZ RDF Pipeline early version based on CSharp
  • Overview
  • 0. Requirements for RDF Data Extractions
  • 1. Create the project data mart
  • 2. De-Identify the data in the data mart
  • 3. Map data in the data mart to target value sets
  • 4. Create standardized RDF (SPHN) views
  • 5. The RDF Ontology and Mapping
  • 6. RDF Extraction into a turtle file
  • 7. RDF Validation
• New Strategic USZ RDF Pipeline using SPHN Connector
  • Overview
  • Definition of Projects
  • Data storage/sources
  • Processes/Functions

Background information

• RDF in SPHN
  • The history
  • Learn about how to use linked data in SPHN
• Semantic Web in SPHN
  • References
• Resource Description Framework (RDF)
  • Introduction to RDF
  • Data Representation with Triples
  • RDF Terms
  • RDF Storage Formats
  • References
• SHACL
  • SHACL targets
  • SHACL shapes
  • SHACL constraints
  • Validation report
  • SHACL example
• SPARQL
  • Introduction
  • Structure of a query
  • Types of queries
  • Query formation

Other

• Contact Information
• Acknowledgements