Ontology (information Science) - Overview

Overview

The term ontology has its origin in philosophy and has been applied in many different ways. The word element onto- comes from the Greek ὤν, ὄντος « being; that which is », present participle of the verb εἰμί « be ». The core meaning within computer science is a model for describing the world that consists of a set of types, properties, and relationship types. Exactly what is provided around these varies, but they are the essentials of an ontology. There is also generally an expectation that there be a close resemblance between the real world and the features of the model in an ontology.

What many ontologies have in common in both computer science and in philosophy is the representation of entities, ideas, and events, along with their properties and relations, according to a system of categories. In both fields, one finds considerable work on problems of ontological relativity (e.g., Quine and Kripke in philosophy, Sowa and Guarino in computer science), and debates concerning whether a normative ontology is viable (e.g., debates over foundationalism in philosophy, debates over the Cyc project in AI). Differences between the two are largely matters of focus. Philosophers are less concerned with establishing fixed, controlled vocabularies than are researchers in computer science, while computer scientists are less involved in discussions of first principles, such as debating whether there are such things as fixed essences or whether entities must be ontologically more primary than processes.

Other fields make ontological assumptions that are sometimes explicitly elaborated and explored. For instance, the definition and ontology of economics (also sometimes called the political economy) is hotly debated especially in Marxist economics where it is a primary concern, but also in other subfields . Such concerns intersect with those of information science when the intent of a simulation or model is to enable decisions in the economic realm, for instance, to determine what capital assets are at risk or how much (see risk management). All social sciences have explicit ontology issues because they do not have hard falsification criteria like most models in physical sciences - indeed the lack of such widely accepted hard falsification criteria is what defines a social or soft science.