The major schools of ontological design can be classified as: Philosophy School; Vocabulary and Taxonomy School; Relational School; Object-Oriented School; Standards School; Linked Data School; NLP/LLM School; and Data-Centric School.
There are a few well-known ontologies that are a hybrid of more than one of these schools. For instance, most of the OBO Life Sciences ontologies are a hybrid of the Philosophy and Taxonomy School, I think this will make more sense after we describe each school individually. The philosophy school aims to ensure that all modeled concepts adhere to strict rules of logic and conform to a small number of well-vetted primitive concepts. The Basic Formal Ontology (BFO), DOLCE, and Cyc are the best-known exemplars of this school.
The vocabulary and taxonomy school tends to start with a glossary of terms from the domain and establish what they mean (vocabulary school) and how these terms are hierarchically related to each other (taxonomy school). The two schools are more alike than different. The taxonomy school especially tends to be based on standards that were created before the Web Ontology Language (OWL). These taxonomies often model a domain as hierarchical structures without defining what a link in the hierarchy means. As a result, they often mix sub-component and sub-class hierarchies.
Relational ontologists tend to be very rigorous about putting specific domains and ranges on all their properties. Properties are rarely reused. All properties will have inverses. Meanwhile, the object-oriented school comes from designers who grew up with object-oriented modeling. OO ontologies tend to co-exist with Graph QL and focus on json output.
The Standards School is a Janus school, with two faces, one facing up and one facing down. The one facing down is concerned with building ontologies that others can (indeed should) reuse. The one facing up is the enterprise ontologies that import the standard ontologies to conform. The Linked Open Data School promotes the idea of sharing identifiers across enterprises. Linked data is very focused on instance (individual or ABox) data, and only secondarily on classes.
The NLP/LLM School turns ontology design over to the machines. Most of these are also internal projects. Meanwhile, the Data-Centric School focuses on ontologies that can be populated and implemented. There isn’t one of these schools that is better than any of the others for all purposes. They each grew up solving different problems and emphasizing different aspects of the problem.
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