Title here
Summary here
Simplified Semantic Data Modeling
An approach for modeling data of multiple domains that enables Subject Matter Experts to contribute to controlled vocabularies with minimal data modeling expertise.
Get StartedWhy S2DM?
Simple
Any Subject Matter Expert can contribute to controlled vocabularies with minimal data modeling expertise.
Semantic
Specifies meaningful data structures, cross-domain relationships, and arbitrary classification schemes.
Interoperable
Built on GraphQL SDL and SKOS standards for maximum compatibility and reusability.
The Simplified Semantic Data Modeling (S2DM) is an approach for modeling data of multiple domains.
It is simple in the sense that any Subject Matter Expert (SME) could contribute to a controlled vocabulary with minimal data modeling expertise.
Likewise, it is semantic in the sense that it specifies meaningful data structures, their cross-domain relationships, and arbitrary classification schemes.
Bear in mind the word Simplified in the name. This approach aims to foster the adoption of (some) good data modeling practices. It does not intend to re-invent or replace long-standing standards, such as those of the Semantic Web. Therefore, it does not incorporate advanced reasoning capabilities or comprehensive ontologies typically associated with traditional semantic data modeling.
The figure above ilustrates the role of the S2DM approach.
One can distinghish three areas:
the re-use of existing resources (left), the artifacts offered by S2DM (center), and the resulting domain data model created and maintained with S2DM artifacts (right).
Getting started
- Get a basic understanding of the S2DM approach.
- Model your domain following the S2DM data modeling guideline.
- Maintain your domain model with the support of the provided S2DM tools.
S2DM artifacts are based on the following existing resources. Getting familiar with them is recommended.
Modeling languages and vocabularies
- GraphQL Schema Definition Language (SDL): Provides a clear, human-readable syntax for defining data structures and relationships, making it easy for SMEs to understand and use without requiring deep technical expertise.
- Simple Knowledge Organization System (SKOS): An RDF-based vocabulary that offers a straightforward framework for creating and managing hierarchical classifications and relationships between concepts, facilitating intuitive and semantically rich knowledge organization.
Tools
- rdflib: For working with RDF data in Python (e.g.,
SKOS). - graphql-core: For working with
GraphQLschemas in Python (e.g.,SDL). - Others
- rdflib: For working with RDF data in Python (e.g.,
