Lecture

Neuroinformatics: Ontologies

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Description

This lecture introduces the concept of ontologies as formal representations of knowledge about specific entities or concepts, focusing on their importance in neuroscience. It covers the role of ontologies in organizing data, defining properties of entities, and establishing relationships between them. Specific examples from the Blue Brain Project are discussed, including the Allen Institute's brain region definitions in mice, Markram's dataset on Rat S1 Hindlimb Cortex, and Blue Brain's ontologies for electrical and synapse types. The lecture also explores the classification of cortical interneuron types and the morpho-electric cell type ontology, providing insights into the complex structure of the brain at a cellular level.

In MOOC

Simulation Neurocience

Official source

Related concepts (32)

Knowledge representation and reasoning

Knowledge representation and reasoning (KRR, KR&R, KR2) is the field of artificial intelligence (AI) dedicated to representing 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 dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge in order to design formalisms that will make complex systems easier to design and build.

Ontology (information science)

In 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. 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. Every academic discipline or field creates ontologies to limit complexity and organize data into information and knowledge.

Ontology

In metaphysics, ontology is the philosophical study of being, as well as related concepts such as existence, becoming, and reality. Ontology addresses questions like how entities are grouped into and which of these entities exist on the most fundamental level. Ontologists often try to determine what the categories or highest kinds are and how they form a system of categories that encompasses the classification of all entities. Commonly proposed categories include substances, properties, relations, states of affairs, and events.

Upper ontology

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) which 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.

Knowledge graph

In knowledge representation and reasoning, knowledge graph is a knowledge base that uses a graph-structured data model or topology to integrate data. Knowledge graphs are often used to store interlinked descriptions of entities - objects, events, situations or abstract concepts - while also encoding the semantics underlying the used terminology. Since the development of the Semantic Web, knowledge graphs are often associated with linked open data projects, focusing on the connections between concepts and entities.

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