I've wrestled with whether to use Property Graphs to store and query the Physics Derivation Graph. I see potential value, but the licensing of Neo4j keeps me from committing. I'm aware of other implementations, but I don't have confidence about either their stability or durability.
This post makes a convincing argument about both the short-comings of a property-graph-based knowledge graph and the value of an RDF-based storage method. To summarize,
- don't be distracted by visualization capabilities; inference is more important
- property graph IDs are local, whereas identifiers in RDF are global.
- Global IDs are vital for enabling federation, merge, diff
I know OWL (Web Ontology Language) is popular for knowledge representation, and this post was the first to provide a clear breakdown of the difference between property graphs, RDF, and OWL. OWL supports
- the ability infer that a node that is a member of a class is also a member of any of its superclasses
- properties can have superproperties
OWL overview:
- https://www.cambridgesemantics.com/blog/semantic-university/learn-rdf/
- https://www.cambridgesemantics.com/blog/semantic-university/learn-owl-rdfs/owl-101/
- https://www.cambridgesemantics.com/blog/semantic-university/learn-owl-rdfs/
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