The point of semantic modeling is to capture all of the detail, all of the knowledge, all of the information that becomes available to us
The point of semantic modeling is to capture all of the detail, all of the knowledge, all of the information that becomes available to us.
Here is an example of extensive semantic model: ontology plus taxonomies for greater depth.
This example is quite a comprehensive semantic model if you consider that it’s supported with nearly 100 sets of definitions and descriptions.
The model describes knowledge of many different terms, along with an understanding of how those terms are defined, described, and interrelated.
When it becomes difficult to absorb all at once, view it in layers:
- Begin with the simple knowledge graph—understand the nodes and the edges, the illustration of things and relationships among them.
- Then view the property graph to understand the facts that can be known about each thing and each relationship.
- Finally, extend it to include taxonomies to see classes and subclasses.
Another approach for layering might begin with the knowledge graph showing things and relationships, then add entity taxonomies to understand classes and subclasses of entities, and finally extend it to see properties and property taxonomies.
Don’t shy away from large or complex models! Simply plan to manage that detail and complexity by layering and segmenting the diagram. This provides the ability to look at subsets of the model without losing the comprehensive view of enterprise semantics.
Graphic sourced from the ‘Architecture and Design for Data Interoperability’ course by Dave Wells. https://lnkd.in/gtqThWdX
The point of semantic modeling is to capture all of the detail, all of the knowledge, all of the information that becomes available to us
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