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as by definition as part one of {4.0L engine, 4.6L engine} The value of an attribute can be a complex data type; in this example, the related engine can only be one of a list of subtypes of engines, not just a single thing. Ontologies are only true ontologies if concepts are related to other concepts (the concepts do have attributes).
For example, CSF can represent Colony Stimulating Factor or Cerebral Spinal Fluid, both of which are represented by the same term, CSF, in biomedical literature. [86] This is why a large number of public ontologies are related to the life sciences.
For example, social ontology examines basic concepts used in the social sciences. Applied ontology is particularly relevant to information and computer science, which develop conceptual frameworks of limited domains. These frameworks facilitate the structured storage of information, such as in a college database tracking academic activities.
In philosophy, a core ontology is a basic and minimal ontology consisting only of the minimal concepts required to understand the other concepts. It must be based on a core glossary in some human language so humans can comprehend the concepts and distinctions made.
In information science, an upper ontology (also known as a top-level ontology, upper model, or foundation ontology) is an ontology (in the sense used in information science) that consists of very general terms (such as "object", "property", "relation") that are common across all domains.
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Ontologies can be used for structuring data in a machine-readable manner. [14] In this context, an ontology is a controlled vocabulary of classes that can be placed in hierarchical relations with each other. [15] These classes can represent entities in the real world which data is about.
In philosophy, the term formal ontology is used to refer to an ontology defined by axioms in a formal language with the goal to provide an unbiased (domain- and application-independent) view on reality, which can help the modeler of domain- or application-specific ontologies to avoid possibly erroneous ontological assumptions encountered in modeling large-scale ontologies.