Ontology (information science) in the context of "ICD-11"

Knowledge representation (KR) aims to model information in a structured manner to formally represent it as knowledge in knowledge-based systems whereas knowledge representation and reasoning (KRR, KR&R, or KR²) also aims to understand, reason, and interpret knowledge. KRR is widely used in the field of artificial intelligence (AI) with the goal to represent information about the world in a form that a computer system can use to solve complex tasks, such as diagnosing a medical condition or having a natural-language dialog. KR incorporates findings from psychology about how humans solve problems and represent knowledge, in order to design formalisms that make complex systems easier to design and build. KRR also incorporates findings from logic to automate various kinds of reasoning.

Traditional KRR focuses more on the declarative representation of knowledge. Related knowledge representation formalisms mainly include vocabularies, thesaurus, semantic networks, axiom systems, frames, rules, logic programs, and ontologies. Examples of automated reasoning engines include inference engines, theorem provers, model generators, and classifiers.

In philosophy, a process ontology refers to a universal model of the structure of the world as an ordered wholeness. Such ontologies are fundamental ontologies, in contrast to the so-called applied ontologies. Fundamental ontologies do not claim to be accessible to any empirical proof in itself but to be a structural design pattern, out of which empirical phenomena can be explained and put together consistently. Throughout Western history, the dominating fundamental ontology is the so-called substance theory. However, fundamental process ontologies have become more important in recent times, because the progress in the discovery of the foundations of physics has spurred the development of a basic concept able to integrate such boundary notions as "energy," "object", and those of the physical dimensions of space and time.

In computer science, a process ontology is a description of the components and their relationships that make up a process. A formal process ontology is an ontology in the knowledge domain of operations. Often such ontologies take advantage of the benefits of an upper ontology. Planning software can be used to perform plan generation based on the formal description of the process and its constraints. Numerous efforts have been made to define a process/planning ontology.

In information science and ontology, a classification scheme is an arrangement of classes or groups of classes. The activity of developing the schemes bears similarity to taxonomy, but with perhaps a more theoretical bent, as a single classification scheme can be applied over a wide semantic spectrum while taxonomies tend to be devoted to a single topic.

In the abstract, the resulting structures are a crucial aspect of metadata, often represented as a hierarchical structure and accompanied by descriptive information of the classes or groups. Such a classification scheme is intended to be used for the classification of individual objects into the classes or groups, and the classes or groups are based on characteristics which the objects (members) have in common.

A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) and provide the core capabilities of modern chatbots. LLMs can be fine-tuned for specific tasks or guided by prompt engineering. These models acquire predictive power regarding syntax, semantics, and ontologies inherent in human language corpora, but they also inherit inaccuracies and biases present in the data they are trained on.

They consist of billions to trillions of parameters and operate as general-purpose sequence models, generating, summarizing, translating, and reasoning over text. LLMs represent a significant new technology in their ability to generalize across tasks with minimal task-specific supervision, enabling capabilities like conversational agents, code generation, knowledge retrieval, and automated reasoning that previously required bespoke systems.

Frames are an artificial intelligence data structure used to divide knowledge into substructures by representing "stereotyped situations".

They were proposed by Marvin Minsky in his 1974 article "A Framework for Representing Knowledge". Frames are the primary data structure used in artificial intelligence frame languages; they are stored as ontologies of sets.

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. An important function of an upper ontology is to support broad semantic interoperability among a large number of domain-specific ontologies by providing a common starting point for the formulation of definitions. Terms in the domain ontology are ranked under the terms in the upper ontology, e.g., the upper ontology classes are superclasses or supersets of all the classes in the domain ontologies.

A number of upper ontologies have been proposed, each with its own proponents.

The Cell Ontology is an ontology that aims at capturing the diversity of cell types in animals. It is part of the Open Biomedical and Biological Ontologies (OBO) Foundry. The Cell Ontology identifiers and organizational structure are used to annotate data at the level of cell types, for example in single-cell RNA-seq studies. It is one important resource in the construction of the Human Cell Atlas.

The Cell Ontology was first described in an academic article in 2005.