Did You Know What You Really Need is an Ontology?
Web 3.0 is labelled the semantic web.
While relational data modelling requires conceptual, logical and physical models along with a deep data dictionary and glossary to facilitate business understanding, a semantic data model integrates all of those into the Ontology. Whether you decide to stick fastidiously to OWL, venture into SHACL or tinker with SKOS, the flexibility and power offered by linked data, using open standards and a handful of (often open-source) tools, is immense.
Two key benefits of semantic data models are linked data and using inference (or reasoning). It’s actually quite difficult to perform such reasoning in relational databases but ontologies excel at this kind of reasoning. It's relatively easy to then develop algorithms that explore your graph for new relationships you hadn’t defined in the model, thus adding to the knowledge graph.
Further, because semantic modelling is concerned with meaning, new relationships provide insights within the context of your domain. This logical reasoning is in contrast to the statistical approach often taken with data-lakes or traditional NLP, which might be good at finding similar concepts, but miss out on different concepts that are relevant or actually share meaning.
Why it’s relevant to Nextspace
Quite simply, ontological relationships are the key to Nextspace’s graph database model, preparing your data for the semantic web.