![]() ![]() Some researchers, drawing inspiration from philosophical ontologies, viewed computational ontology as a kind of applied philosophy. ![]() In particular, David Powers introduced the word ontology to AI to refer to real world or robotic grounding, publishing in 1990 literature reviews emphasizing grounded ontology in association with the call for papers for a AAAI Summer Symposium Machine Learning of Natural Language and Ontology, with an expanded version published in SIGART Bulletin and included as a preface to the proceedings. In the 1980s, the AI community began to use the term ontology to refer to both a theory of a modeled world and a component of knowledge-based systems. AI researchers argued that they could create new ontologies as computational models that enable certain kinds of automated reasoning, which was only marginally successful. Since the mid-1970s, researchers in the field of artificial intelligence (AI) have recognized that knowledge engineering is the key to building large and powerful AI systems. Metaphysics has been an ongoing topic of discussion since recorded history. One of five traditional branches of philosophy, metaphysics is concerned with exploring existence through properties, entities and relations such as those between particulars and universals, intrinsic and extrinsic properties, or essence and existence. Ontologies arise out of the branch of philosophy known as metaphysics, which deals with questions like "what exists?" and "what is the nature of reality?". Etymology įurther information: Artificial intelligence § History Artificial intelligence has retained the most attention regarding applied ontology in subfields like natural language processing within machine translation and knowledge representation, but ontology editors are being used often in a range of fields like education without the intent to contribute to AI. In both fields, there is considerable work on problems of ontology engineering (e.g., Quine and Kripke in philosophy, Sowa and Guarino in computer science), and debates concerning to what extent normative ontology is possible (e.g., foundationalism and coherentism in philosophy, BFO and Cyc in artificial intelligence).Īpplied ontology is considered a successor to prior work in philosophy, however many current efforts are more concerned with establishing controlled vocabularies of narrow domains than first principles, the existence of fixed essences or whether enduring objects (e.g., perdurantism and endurantism) may be ontologically more primary than processes. What ontologies in both computer science and philosophy have in common is the attempt to represent entities, ideas and events, with all their interdependent properties and relations, according to a system of categories. An example of economics relying on information science occurs in cases where a simulation or model is intended to enable economic decisions, such as determining what capital assets are at risk and by how much (see risk management). įor instance, the definition and ontology of economics is a primary concern in Marxist economics, but also in other subfields of economics. 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. New ontologies may improve problem solving within that domain. More simply, an ontology is a way of showing the properties of a subject area and how they are related, by defining a set of concepts and categories that represent the subject.Įvery academic discipline or field creates ontologies to limit complexity and organize data into information and knowledge.Įach uses ontological assumptions to frame explicit theories, research and applications. In computer science and information science, an ontology encompasses a representation, formal naming, and definition of the categories, properties, and relations between the concepts, data, and entities that substantiate one, many, or all domains of discourse. ![]()
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