AIDOC-AP - AI Documentation Application Profile
type: Ontology
contributor: Timea Pahi
  type: Person
  organization: University of Applied Sciences St. Pölten
creator: Sebastian Neumaier
  type: Person
  organization: University of Applied Sciences St. Pölten
creator: Tobias Dam
  type: Person
  organization: University of Applied Sciences St. Pölten
creator: Fabian Kovac
  type: Person
  organization: University of Applied Sciences St. Pölten
description: AIDOC-AP is an application profile for representing the technical documentation of AI systems required under Article 11 and Annex IV of the EU AI Act in a structured, machine-readable form. It reuses established Semantic Web vocabularies (PROV-O, DCAT, MLSchema, AIRO, VAIR, DPV) where possible and adds Annex IV-specific terms, so that provider documentation can be captured as a knowledge graph and checked for completeness against the regulation.
issued: 2025-12-04
language: en
license: https://creativecommons.org/licenses/by/4.0/
modified: 2026-07-07
publisher: CERTAIN Project
preferred namespace prefix: aidoc
label: AIDOC-AP
prior version: https://w3id.org/aidoc-ap/1.0
version IRI: https://w3id.org/aidoc-ap/1.1
version info: 1.1
funding: https://cordis.europa.eu/project/id/101189650
keywords: AI systems, ontology, AIDOC-AP, AI lifecycle, AI Act

AI Activity
type: Class
comment: A superclass for all activities in the AI lifecycle, derived from prov:Activity.
subclass of: Activity

AI Agent
type: Class
comment: A superclass for all agents involved in the AI lifecycle, derived from prov:Agent.
subclass of: Agent

AI Artifact
type: Class
comment: A superclass for all tangible inputs and outputs of the AI lifecycle, derived from prov:Entity for provenance tracking.
subclass of: Entity
narrow match: Input
narrow match: Output

AI Deployer
type: Class
source: EU AI Act 2024/1689, Article 3(4)
comment: Any natural or legal person, public authority, agency or other body using an AI system under its authority except where the AI system is used in the course of a personal non-professional activity
subclass of: Agent
subclass of: Organization
subclass of: AI Agent
subclass of: AIDeployer

AI Method
type: Class
comment: Reflects a mathematical algorithm which computes input data to execute an AI task.
subclass of: Algorithm
broad match: AITechnique

AI Method Category
type: Class
comment: A conceptual grouping of AI methods based on shared characteristics, such as machine learning, search optimization, or knowledge representation.

AI Model
type: Class
comment: A computational representation that enables an AI method to execute an AI task.
subclass of: Model
subclass of: AI Artifact

AI Provider
type: Class
source: EU AI Act 2024/1689, Article 3(3)
comment: A natural or legal person, public authority, agency or other body that develops an AI system or a general-purpose AI model or that has an AI system or a general-purpose AI model developed and places it on the market or puts the AI system into service under its own name or trademark, whether for payment or free of charge.
subclass of: Agent
subclass of: Organization
subclass of: AI Agent
subclass of: AIProvider

AI Researcher
type: Class
comment: A person involved in the technical development of an AI system.
subclass of: Agent
subclass of: Person
subclass of: AI Agent
broad match: AIDeveloper

AI System
type: Class
source: EU AI Act 2024/1689, Article 3(1)
comment: An AI system is a machine-based system that is designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment, and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.
subclass of: Entity
subclass of: AISystem

AI System Capability
type: Class
comment: Represents a category of tasks or functions that an AI system can perform, such as sensory, knowledge, or control action processing.

AI Task
type: Class
comment: An action to achieve a specific goal using an AI model.

Action Control Processing
type: NamedIndividual
type: AI System Capability
broader: AI System Capability
definition: Action control processing refers to AI system capabilities that enable movement and control through mechanical, electronic, or software components, allowing simulation and execution of human-like behaviors, object manipulation, and natural gestures with high precision.

Auditor
type: Class
comment: An organization or person responsible for reviewing the AI system for compliance.
subclass of: Agent
subclass of: Organization
subclass of: Person
subclass of: AI Agent

Build and Integration Testing
type: Class
comment: Activity of building system components and conducting integration testing.
subclass of: Software Code Pipeline

Change Log
type: Class
comment: A log of changes made to the system during its lifecycle.
subclass of: Entity

Change Record
type: Class
comment: A record of a change, including reason, start and end date, stakeholders, and the change itself.
subclass of: Entity

Classification
type: NamedIndividual
type: AI Method Category
broader: Supervised Learning
definition: Techniques that assign input data to predefined categories or classes.

Cloud service
type: NamedIndividual
type: Modality
broader: Software service / API
definition: The AI system is provided as a managed cloud service.

Clustering
type: NamedIndividual
type: AI Method Category
broader: Unsupervised Learning
definition: Techniques that group similar data points together based on their features.

Communication Processing
type: NamedIndividual
type: AI System Capability
broader: AI System Capability
definition: Communication processing encompasses AI system capabilities for transmitting and exchanging information, either unidirectionally without feedback or bidirectionally and omnidirectionally with feedback, enabling content generation and interactive communication in individual or group contexts.

Computational Resource
type: Class
comment: A computational resource used during the AI lifecycle, such as GPUs, CPUs, clusters or cloud instances.
subclass of: Entity
narrow match: HardwarePlatform

Conversational Learning
type: NamedIndividual
type: AI Method Category
broader: Hybrid Learning
definition: Methods that involve interactive learning through dialogue or feedback from users or other agents.

Data Acquisition Activity
type: Class
comment: The activity of obtaining data from various sources.
subclass of: Data Pipeline

Data Cleaning Procedure
type: Class
comment: A procedure describing how data cleaning is performed, such as outlier detection and removal of invalid records.
subclass of: Data Wrangling/Cleaning

Data Monitoring and Logging
type: Class
comment: Activity of capturing system events, performance, and traceability.
subclass of: Data Pipeline

Data Pipeline
type: Class
comment: The pipeline for data-centric activities in the AI lifecycle.
subclass of: AI Activity

Data Processing
type: Class
comment: Activity of combining, preprocessing data resulting in a different dataset.
subclass of: Data Pipeline

Data Sheet
type: Class
comment: Structured documentation for a dataset or training configuration, including provenance, scope and main characteristics.
subclass of: Entity

Data Testing
type: Class
source: EU AI Act 2024/1689, Article 3(32)
comment: Activity of using datasets for model testing. Testing data means data used for providing an independent evaluation of the AI system in order to confirm the expected performance of that system before its placing on the market or putting into service.
subclass of: Data Pipeline

Data Training
type: Class
source: EU AI Act 2024/1689, Article 3(29)
comment: Activity of using datasets for model training. Training data means data used for training an AI system through fitting its learnable parameters.
subclass of: Data Pipeline

Data Validation
type: Class
source: EU AI Act 2024/1689, Article 3(30)
comment: Activity of using datasets for model validation. Validation data means data used for providing an evaluation of the trained AI system and for tuning its non-learnable parameters and its learning process in order, inter alia, to prevent underfitting or overfitting.
subclass of: Data Pipeline

Data Wrangling/Cleaning
type: Class
comment: Activity of ensuring data quality through cleaning, filtering, and enrichment.
subclass of: Data Pipeline

Dataset
type: Class
comment: Represents a dataset used in the AI lifecycle.
subclass of: Dataset
subclass of: AI Artifact

Decision Making
type: NamedIndividual
type: AI Method Category
broader: Traditional AI Techniques
definition: Approaches for selecting optimal actions under constraints or uncertainty.

Declaration of Conformity
type: Class
comment: A document declaring the system's conformity with standards, such as the EU Declaration of Conformity.
subclass of: Entity
narrow match: EUDeclarationOfConformity

Decommissioning
type: Class
comment: Activity of taking a model out of production.
subclass of: Software Code Pipeline

Deployment
type: Class
comment: Activity of delivering the AI system to production environments.
subclass of: Software Code Pipeline

Dimension Reduction
type: NamedIndividual
type: AI Method Category
broader: Unsupervised Learning
definition: Techniques that reduce the number of features in the data while preserving important information.

Embedded component
type: NamedIndividual
type: Modality
definition: The AI system is provided as a component embedded within a larger software or hardware product.

Explainable AI Feature
type: Class
comment: A specific feature designed to provide explanations for the AI system's output.
subclass of: Transparency Measure

Exploration and Validation
type: Class
comment: Activity of reviewing data requirements, sources, and initial assessments.
subclass of: Data Pipeline

Graph-Based Semi-Supervised Learning
type: NamedIndividual
type: AI Method Category
broader: Semi-Supervised Learning

Hardware Component
type: Class
comment: Represents the hardware required for an AI system to run.
subclass of: Entity

Hardware product
type: NamedIndividual
type: Modality
definition: The AI system is provided as, or integrated into, a physical hardware product.

Human Oversight Mechanism
type: Class
comment: A mechanism or measure to facilitate human intervention or supervision.
subclass of: Entity

Hybrid Learning
type: NamedIndividual
type: AI Method Category
broader: AI Method Category
definition: Methods that combine different learning paradigms, such as hybrid neuronal systems, learning with knowledge, and conversational learning.

Hybrid Neuronal Systems
type: NamedIndividual
type: AI Method Category
broader: Hybrid Learning
definition: Methods that combine neural architectures with symbolic reasoning or other AI techniques.

Intended Purpose
type: Class
source: EU AI Act 2024/1689, Article 3(12)
comment: The use for which an AI system is intended by the provider, including the specific context and conditions of use, as specified in the instructions for use, promotional or sales materials and statements, and the technical documentation. Modelled as a class so that intended purposes can be related (e.g. via skos:broader/narrower), compared and tracked across system versions.
subclass of: IntendedPurpose

Interface
type: Class
comment: The interface provided for users or operators of an AI system.
subclass of: Entity
subclass of: AIComponent

Knowledge Processing
type: NamedIndividual
type: AI System Capability
broader: AI System Capability
definition: Knowledge processing refers to AI system capabilities for handling different types of knowledge—factual, conceptual, procedural, and metacognitive—through activities such as recognizing patterns, classifying information, generating concepts, executing procedures, and applying reflective reasoning to solve problems and adapt to new situations.

Knowledge Representation
type: NamedIndividual
type: AI Method Category
broader: Symbolic AI
definition: Methods for structuring and organizing knowledge, including ontologies, semantic networks, and knowledge graphs.

Labeling Procedure
type: Class
comment: An activity describing how data labels were assigned, including annotators, tools and guidelines.
subclass of: Data Pipeline

Learning with Knowledge
type: NamedIndividual
type: AI Method Category
broader: Hybrid Learning
definition: Techniques that integrate both symbolic or explicit knowledge representation and machine learning approaches.

Log
type: Class
comment: A record of system events, performance, and traceability.
subclass of: Entity

Logical Reasoning
type: NamedIndividual
type: AI Method Category
broader: Symbolic AI
definition: Methods that use rules and principles for formal as well as interactive verification to identify patterns and logical structures and make logical deductions.

Machine Learning Pipeline
type: Class
comment: The pipeline for machine learning-centric activities in the AI lifecycle.
subclass of: AI Activity

Machine Learning
type: NamedIndividual
type: AI Method Category
broader: AI Method Category
definition: Methods that learn patterns from data, including supervised, unsupervised, semi-supervised, self-supervised, and reinforcement learning.

Modality
type: Class
source: EU AI Act 2024/1689, Annex IV(1)
comment: The form in which the AI system is provided or placed on the market (e.g. standalone software, a software service/API, a component embedded in a product, or a hardware product). Modelled as a class with a controlled vocabulary so provision forms can be enumerated, compared and reused across systems.

Model-Based Reinforcement Learning
type: NamedIndividual
type: AI Method Category
broader: Reinforcement Learning

Model Engineering
type: Class
comment: Activity of defining architecture, selecting algorithms, and training the model.
subclass of: Experiment
subclass of: Machine Learning Pipeline

Model Evaluation
type: Class
comment: Activity of assessing performance, robustness, and accuracy.
subclass of: Machine Learning Pipeline

Model-Free Reinforcement Learning
type: NamedIndividual
type: AI Method Category
broader: Reinforcement Learning

Model Packaging
type: Class
comment: Activity of preparing the trained model for deployment.
subclass of: Machine Learning Pipeline

Model Versioning
type: Class
comment: Activity of managing revisions and tracking changes in model iterations.
subclass of: Machine Learning Pipeline

Modified Learning Concepts
type: NamedIndividual
type: AI Method Category
broader: Semi-Supervised Learning

Non-Probabilistic Reasoning
type: NamedIndividual
type: AI Method Category
broader: Symbolic AI
definition: Techniques that do not rely on probabilistic models, such as qualitative procedures, rule-based approaches, reasoning with uncertainty as well as reasoning with belief functions.

Optimization
type: NamedIndividual
type: AI Method Category
broader: Traditional AI Techniques
definition: Methods for improving solutions iteratively, including heuristic and metaheuristic approaches.

Performance Metric
type: Class
comment: A metric used to evaluate the performance of an AI system.
subclass of: Metric

Planning
type: NamedIndividual
type: AI Method Category
broader: Traditional AI Techniques
definition: Approaches for autonomous and semi-autonomous planning procedures as well as plan recognition.

Post-market Monitoring Activity
type: Class
comment: Activity of monitoring the system's performance and behavior after deployment.
subclass of: AI Activity

Post-market Performance Evaluation Activity
type: Class
comment: Activity of evaluating system performance in the post-market phase.
subclass of: AI Activity

Probabilistic Reasoning
type: NamedIndividual
type: AI Method Category
broader: Symbolic AI
definition: Techniques that handle uncertainty and incomplete information using probabilistic models, such as Bayesian networks and Markov decision processes.

Regression
type: NamedIndividual
type: AI Method Category
broader: Supervised Learning
definition: Techniques that predict continuous output values based on input data.

Reinforcement Learning
type: NamedIndividual
type: AI Method Category
broader: Machine Learning
definition: Techniques that learn optimal actions through trial and error by receiving feedback in the form of rewards or penalties.

Search
type: NamedIndividual
type: AI Method Category
broader: Traditional AI Techniques
definition: Techniques for exploring state spaces to find solutions.

Self-Supervised Learning
type: NamedIndividual
type: AI Method Category
broader: Machine Learning
definition: Techniques that generate their own labels from the input data to learn useful representations.

Semi-Supervised Learning
type: NamedIndividual
type: AI Method Category
broader: Machine Learning
definition: Techniques that combine a small amount of labeled data with a large amount of unlabeled data during training.

Sensory Input Processing
type: NamedIndividual
type: AI System Capability
broader: AI System Capability
definition: Internal sensory processing refers to AI system capabilities for interpreting data about its own internal states, such as position, orientation, pose, spatial configuration, or physical integrity, enabling self-monitoring and maintaining stability or optimal functioning.

Software Code Pipeline
type: Class
comment: The pipeline for software development-centric activities in the AI lifecycle.
subclass of: AI Activity

Software Component
type: Class
comment: A logical or physical software component of the AI system, such as a service, module, or microservice participating in the overall processing.
subclass of: AI Artifact

Software Dependency
type: Class
comment: A software package or library that the AI system depends on.
subclass of: Software
subclass of: Entity
subclass of: AI Artifact

Software Development
type: Class
comment: Activity of developing and writing software code for the AI system.
subclass of: Software Code Pipeline

Software Implementation
type: Class
comment: Represents the concrete, executable software that realizes an algorithm.
subclass of: Implementation
subclass of: AI Artifact

Software service / API
type: NamedIndividual
type: Modality
definition: The AI system is provided as a hosted service consumed over an interface such as a web API.

Software Versioning
type: Class
comment: Activity of managing revisions and tracking changes in software iterations.
subclass of: Software Code Pipeline

Standalone software
type: NamedIndividual
type: Modality
definition: The AI system is provided as a standalone software application installed and run by the deployer.

Standard
type: Class
comment: A harmonised or other standard applied to the AI system.
subclass of: Entity
subclass of: Standard

Statistical Approaches
type: NamedIndividual
type: AI Method Category
broader: Semi-Supervised Learning

Supervised Learning
type: NamedIndividual
type: AI Method Category
broader: Machine Learning
definition: Techniques that learn from labeled data to make predictions or classifications.

Symbolic AI
type: NamedIndividual
type: AI Method Category
broader: AI Method Category
definition: Methods that use knowledge representation and logic-based reasoning to model knowledge and make inferences.

Traditional AI Techniques
type: NamedIndividual
type: AI Method Category
broader: AI Method Category
definition: Algorithmic strategies for search, optimization, planning and decision making

Training Data Sheet
type: Class
comment: A data sheet focusing on training methodologies, techniques, hyperparameters and dataset usage for training, validation and testing.
subclass of: Data Sheet

Transparency Measure
type: Class
comment: A feature or measure to ensure the transparency of an AI system.
subclass of: Entity

Unsupervised Learning
type: NamedIndividual
type: AI Method Category
broader: Machine Learning
definition: Techniques that identify patterns and structures in unlabeled data.

Visual Documentation
type: Class
comment: Visual documentation of an AI system including photographs, illustrations, diagrams, or renderings that show external features, internal layout, or markings as required by Annex IV.
subclass of: Entity

affects
type: ObjectProperty
comment: Links an activity to the artifact it affects or modifies.
domain: AI Activity
range: AI Artifact

annotation tool
type: DatatypeProperty
comment: Tool or platform used to perform annotation (e.g., internal tool, external labeling platform).
domain: Labeling Procedure
range: string

annotator type
type: DatatypeProperty
comment: Type of annotators involved in the labeling procedure (e.g., domain experts, laypersons, crowd workers).
domain: Labeling Procedure
range: string

applies standard
type: ObjectProperty
comment: Links an AI system to a standard it has applied for compliance.
domain: AI System
range: Standard

data characteristic
type: DatatypeProperty
comment: Project-specific summary of key characteristics of the dataset, such as feature types, label distributions, data modalities or known biases. For interoperable descriptions, individual variables SHOULD be modeled with schema:variableMeasured and quality aspects with dqv:hasQualityMeasurement / dqv:hasQualityAnnotation.
domain: Dataset
range: string

data collection method
type: DatatypeProperty
comment: Project-specific description of the method used to collect the data (e.g., manual annotation, web scraping, sensor logs). For interoperability, this SHOULD be complemented with dcterms:provenance and, where appropriate, schema:measurementTechnique.
domain: Dataset
range: string

data scope
type: DatatypeProperty
comment: Project-specific summary of the scope of the data contained in the dataset (e.g., population, geography, time period). For interoperable modeling, more specific aspects SHOULD be captured using dcterms:spatial, dcterms:temporal and dcterms:coverage.
domain: Dataset
range: string

data selection criteria
type: DatatypeProperty
comment: Project-specific description of inclusion and exclusion criteria or sampling strategy used to select data for the dataset. Related sampling and bias information MAY also be represented using dqv:hasQualityAnnotation on the dataset or a dedicated sampling-plan resource.
domain: Dataset
range: string

depends on
type: ObjectProperty
comment: Links a system or component to its software.
domain: Class
  union of: AI Artifact
  union of: AI System
range: AI Artifact
sub property of: hasPart

depicts
type: DatatypeProperty
comment: Description of what the visual documentation shows (e.g., 'external view', 'internal layout', 'marking placement').
domain: Visual Documentation
range: string

end date
type: DatatypeProperty
comment: The end date of the change.
domain: Change Record
range: date

evaluation method
type: DatatypeProperty
comment: The methodology used to evaluate a performance metric.
domain: Model Evaluation
range: string

feeds into component
type: ObjectProperty
comment: Indicates that the output of one software component is used as input by another, forming part of the system's processing flow.
domain: Software Component
range: Software Component

has AI activity
type: ObjectProperty
comment: Links an AI Lifecycle Phase to a specific AI Activity.
domain: AILifecyclePhase
range: AI Activity

has architecture
type: DatatypeProperty
comment: Links a system or model to a description of its architecture.
domain: Class
  union of: AI Model
  union of: AI System
range: Literal

has capability
type: ObjectProperty
comment: Relates an AISystem to an AISystemCapability it is designed to exhibit or support
domain: AI System
range: AI System Capability

has changelog
type: ObjectProperty
comment: Links an AI system to its change log.
domain: AI System
range: Change Log

has change record
type: ObjectProperty
comment: Links a ChangeLog to one or more ChangeRecords.
domain: Change Log
range: Change Record

has component
type: ObjectProperty
comment: Links an AISystem to its constituent components like models and datasets.
domain: AI System
range: Class
  union of: AI Artifact
  union of: AIComponent

has declaration of conformity
type: ObjectProperty
comment: Links an AI system to its EU Declaration of Conformity document.
domain: AI System
range: Declaration of Conformity

has Frequency
type: ObjectProperty
comment: Specifies the frequency or recurrence pattern at which an AI activity is performed.
domain: AI Activity
range: Frequency

has human oversight
type: ObjectProperty
comment: Links an AI system to a mechanism for human oversight or intervention.
domain: AI System
range: Human Oversight Mechanism

has intended purpose
type: ObjectProperty
source: EU AI Act 2024/1689, Article 3(12)
comment: Links an AI system to its intended purpose, modelled as an aidoc:IntendedPurpose resource rather than a plain string, so that purposes can be linked, compared and tracked across system versions.
domain: AI System
range: Intended Purpose

has interface
type: ObjectProperty
comment: Links an AI system to the interface it provides to users or operators.
domain: AI System
range: Interface

has lifecycle stage
type: ObjectProperty
comment: Links an AI system to its various phases throughout its entire lifecycle.
domain: AI System
range: AILifecyclePhase

has modality
type: ObjectProperty
comment: Links an AI system to its form of provision, modelled as a resource rather than a plain string. The datatype property aidoc:modality is retained as a string shortcut for text-based documentation workflows.
domain: AI System
range: Modality

has performance metric
type: ObjectProperty
comment: Links an evaluation activity to the performance metric it uses.
domain: Model Evaluation
range: Performance Metric

has responsible developer
type: ObjectProperty
comment: Links a software development activity to the developers responsible for it.
domain: Software Development
range: Class
  union of: AI Researcher
  union of: AIDeveloper

has risk
type: ObjectProperty
source: EU AI Act 2024/1689, Article 3(2)
comment: Links an AI artifact (e.g., model or dataset) or an AI system to an associated risk. An AI risk can be modelled using the AIRO ontology. Risk means the combination of the probability of an occurrence of harm and the severity of that harm.
domain: Class
  union of: AI Artifact
  union of: AI System
range: Risk

has software component
type: ObjectProperty
comment: Links an AI system to its software components, such as services or modules that participate in the overall processing.
domain: AI System
range: Software Component
sub property of: has component

has source dataset
type: ObjectProperty
comment: Links a DataPipeline activity to its source datasets.
domain: Data Pipeline
range: Dataset

has stakeholder
type: ObjectProperty
comment: Links the change record to stakeholders (airo:stakeholder or aidoc-ap:AIAgent).
domain: Change Record
range: Class
  union of: AI Agent
  union of: Stakeholder

has transparency measure
type: ObjectProperty
comment: Links an AI system to measures in place to ensure transparency.
domain: AI System
range: Transparency Measure

has visual documentation
type: ObjectProperty
comment: Links an AI system to visual documentation such as photographs, diagrams, or illustrations showing external features, markings, and internal layout.
domain: AI System
range: Visual Documentation

implements task
type: ObjectProperty
comment: Links an AIMethod to the AITask it is designed to perform.
domain: AI Method
range: AI Task

is deployed by
type: ObjectProperty
comment: Links an AI system to its deployer.
domain: AI System
range: AI Deployer

is provided by
type: ObjectProperty
comment: Links an AI system to its provider.
domain: AI System
range: AI Provider

is stored at
type: ObjectProperty
comment: Links an AI artifact to the location or entity where it is stored.
domain: AI Artifact
range: Entity

labeling guideline
type: DatatypeProperty
comment: Guidelines or instructions provided to annotators when assigning labels.
domain: Labeling Procedure
range: string

logs activity
type: ObjectProperty
comment: Links a monitoring activity to the type of activity that is logged.
domain: Data Monitoring and Logging
range: Activity

missing data strategy
type: DatatypeProperty
comment: Strategy for handling missing data (e.g., imputation, deletion, flagging).
domain: Data Cleaning Procedure
range: string

outlier handling method
type: DatatypeProperty
comment: Methods used to identify and handle outliers or other problematic records in the data.
domain: Data Cleaning Procedure
range: string

produces dataset
type: ObjectProperty
comment: Links a DataPipeline activity to the finished dataset it produces.
domain: Data Pipeline
range: Dataset

produces log
type: ObjectProperty
comment: Links a monitoring activity to the log it produces.
domain: Data Monitoring and Logging
range: Log

provides computational resource
type: ObjectProperty
comment: Links a hardware component to the computational resource it provides.
domain: Hardware Component
range: Computational Resource

quality metrics used
type: DatatypeProperty
comment: Data quality metrics or thresholds used to assess the dataset after cleaning.
domain: Data Cleaning Procedure
range: string

reason for change
type: DatatypeProperty
comment: The reason for the change.
domain: Change Record
range: string

records change
type: ObjectProperty
comment: Links the change record to the change (airo:Change).
domain: Change Record
range: Change

requires hardware
type: ObjectProperty
comment: Links a system or artifact to the hardware components or servers it needs to run.
domain: Class
  union of: AI Artifact
  union of: AI System
range: Class
  union of: Server
  union of: Hardware Component
related match: runsOnHardware

resource configuration
type: DatatypeProperty
comment: Configuration details of the computational resource (e.g., number and type of GPUs, CPU cores, memory, accelerator type).
domain: Computational Resource
range: string

resource type
type: DatatypeProperty
comment: Type of computational resource used (e.g., GPU cluster, CPU server, cloud instance).
domain: Computational Resource
range: string

resource vendor
type: DatatypeProperty
comment: Provider or vendor of the computational resource (e.g., cloud provider, hardware manufacturer).
domain: Computational Resource
range: string

start date
type: DatatypeProperty
comment: The start date of the change.
domain: Change Record
range: date

stores
type: ObjectProperty
comment: Links a storage location or entity to the AI artifacts it stores.
domain: Entity
range: AI Artifact
inverse of: is stored at

stores logs at
type: ObjectProperty
comment: Links a monitoring activity to where its logs are stored.
domain: Data Monitoring and Logging
range: Entity

supports capability
type: ObjectProperty
comment: Indicates that an AITask contributes to achieving a broader AISystemCapability.
domain: AI Task
range: AI System Capability

uses AI method
type: ObjectProperty
comment: Links an AI model to the specific AI method it is based on.
domain: AI Model
range: AI Method
related match: AITechnique

uses test data
type: ObjectProperty
comment: Links a testing activity to the dataset used for testing.
domain: Data Testing
range: Dataset
related match: hasTrainingData

uses training data
type: ObjectProperty
comment: Links a training activity to the dataset used for training.
domain: Data Training
range: Dataset
related match: hasTrainingData

uses validation data
type: ObjectProperty
comment: Links a validation activity to the dataset used for validation.
domain: Data Validation
range: Dataset
related match: hasValidationData

version
type: DatatypeProperty
comment: The version of the AI system or artifact.
domain: Entity
range: string