Alternative names: AP, context-specific semantic data specification
Definition: Semantic data specification aimed to facilitate the data exchange in a well-defined application context.
Additional info: It re-uses concepts from one or more semantic data specifications, while adding more specificity, by identifying mandatory, recommended, and optional elements, addressing particular application needs, and providing recommendations for controlled vocabularies to be used.
Source/Reference: SEMIC Style Guide

Alternative names: conceptual model specification
Definition: An abstract representation of a system that comprises well-defined concepts, their qualities or attributes, and their relationships to other concepts.
Additional info: A system is a group of interacting or interrelated elements that act according to a set of rules to form a unified whole.
Source/Reference: SEMIC Style Guide

Alternative names: CV
Definition: A basic, reusable and extensible semantic data specification that captures the fundamental characteristics of an entity in a context-neutral fashion.
Additional info: Its main objective is to provide terms to be reused in the broadest possible context.
Source/Reference: SEMIC Style Guide

Definition: A structured representation of data elements and relationships used to facilitate semantic interoperability within and across domains.
Additional info: Data models represent common languages to facilitate semantic interoperability in a data space, including ontologies, data models, schema specifications, mappings and API specifications that can be used to annotate and describe data sets and data services. They are often domain-specific.
Source/Reference: Data Spaces Blueprint

Alternative names: data schema
Definition: Information exchange data model is a technology-specific framework for data exchange, detailing the syntax, structure, data types, and constraints necessary for effective data communication between systems. It serves as a practical blueprint for implementing an application profile in specific data exchange contexts.
Additional info: An ontology and an exchange data model serve distinct yet complementary roles across different abstraction levels within data management systems. While a Data Schema specifies the technical structure for storing and exchanging data, primarily concerned with the syntactical and structural aspects of data, it is typically articulated using metamodel standards such as JSON Schema and XML Schema.
In contrast, ontologies and data shapes operate at a higher conceptual level, outlining the knowledge and relational dynamics within a particular domain without delving into the specifics of data storage or structural implementations. Although a Data Schema can embody certain elements of an ontology or application profile—particularly attributes related to data structure and cardinalities necessary for data exchange—it does not encapsulate the complete semantics of the domain as expressed in an ontology.
Thus, while exchange data models are essential for the technical realisation of data storage and exchange, they do not replace the broader, semantic understanding provided by ontologies. The interplay between these layers ensures that data schemas contribute to a holistic data management strategy by providing the necessary structure and constraints for data exchange, while ontologies offer the overarching semantic framework that guides the meaningful interpretation and utilisation of data across systems. Together, they facilitate a structured yet semantically rich data ecosystem conducive to advanced data interoperability and effective communication.
Source/Reference: Data Spaces Blueprint

Alternative names: specification artefact, artefact
Definition: A materialisation of a semantic data specification in a concrete representation that is appropriate for addressing one or more concerns (e.g. use cases, requirements).
Source/Reference: SEMIC Style Guide

Alternative names: specification document
Definition: The human-readable representation of an ontology, a data shape, or a combination of both.
Additional info: A semantic data specification document is created with the objective of making it simple for the end-user to understand (a) how a model encodes knowledge of a particular domain, and (b) how this model can be technically adopted and used for a purpose. It is to serve as technical documentation for anyone interested in using (e.g. adopting or extending) a semantic data specification.
Source/Reference: SEMIC Style Guide

Alternative names: data shape constraint specification, data shape constraint, data shape
Definition: A set of conditions on top of an ontology, limiting how the ontology can be instantiated.
Additional info: The conditions and constraints that apply to a given ontology are provided as shapes and other constructs expressed in the form of an RDF graph. We assume that the data shapes are expressed in SHACL language.
Source/Reference: SEMIC Style Guide

Alternative names: ontology specification
Definition: A formal specification describing the concepts and relationships that can formally exist for an agent or a community of agents (e.g. domain experts)
Additional info: It 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.
Source/Reference: SEMIC Style Guide

Alternative names: data specification
Definition: An union of machine- and human-readable artefacts addressing clearly defined concerns, interoperability scope and use-cases.
Additional info: A semantic data specification comprises at least an ontology and a data shape (or either of them individually) accompanied by a human-readable data specification document.
Source/Reference: SEMIC Style Guide

Definition: An established list of preferred terms that signify concepts or relationships within a domain of discourse. All terms must have an unambiguous and non-redundant definition. Optionally it may include synonyms, notes and their translation to multiple languages.

Definition: An upper ontology is a highly generalised ontology that includes very abstract concepts applicable across all domains, such as "object," "property," and "relation." Its primary role is to facilitate broad semantic interoperability among numerous domain-specific ontologies by offering a standardised foundational framework. This framework assists in harmonising diverse domain ontologies, allowing for consistent data interpretation and efficient information exchange.