Computer science ontology

How is ontology used in AI?

Ontologies are formal representations of the concepts, relations, and constraints in a domain of knowledge.
They are widely used in artificial intelligence (AI) to provide a common vocabulary, structure, and reasoning for various tasks and applications..

What is an ontology in computer science?

In the context of computer and information sciences, an ontology defines a set of representational primitives with which to model a domain of knowledge or discourse.
The representational primitives are typically classes (or sets), attributes (or properties), and relationships (or relations among class members)..

What is ontology in data science?

An ontology is a formal description of knowledge as a set of concepts within a domain and the relationships that hold between them.
It ensures a common understanding of information and makes explicit domain assumptions thus allowing organizations to make better sense of their data..

What is ontology in science?

In brief, ontology, as a branch of philosophy, is the science of what is, of the kinds and structures of objects.
In simple terms, ontology seeks the classification and explanation of entities.
Ontology is about the object of inquiry, what you set to examine..

What is ontology with example?

At its core, ontology is the study of what is.
To make this a little more concrete, one could also say ontology is the study of what exists or of what is real. “Does God exist?,” “Are my feelings real?”, “What is 'nothing,' and does it exist?” are all examples of ontological questions..

Why is ontology important in computer science?

The key role of ontologies with respect to database systems is to specify a data modeling representation at a level of abstraction above specific database designs (logical or physical), so that data can be exported, translated, queried, and unified across independently developed systems and services..

Why is ontology important in information science?

Improved ontologies may improve problem solving within that domain, interoperability of data systems, and discoverability of data.
Translating research papers within every field is a problem made easier when experts from different countries maintain a controlled vocabulary of jargon between each of their languages..

The following are the advantages of Ontologies: