University of Applied Sciences St. Pölten
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.
AIDOC-AP - AI Documentation Application Profile
AIDOC-AP
AI systems, ontology, AIDOC-AP, AI lifecycle, AI Act
A superclass for all activities in the AI lifecycle, derived from prov:Activity.
AI Activity
A superclass for all agents involved in the AI lifecycle, derived from prov:Agent.
AI Agent
A superclass for all tangible inputs and outputs of the AI lifecycle, derived from prov:Entity for provenance tracking.
AI Artifact
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
AI Deployer
Reflects a mathematical algorithm which computes input data to execute an AI task.
AI Method
A conceptual grouping of AI methods based on shared characteristics, such as machine learning, search optimization, or knowledge representation.
A computational representation that enables an AI method to execute an AI task.
AI Model
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.
AI Provider
A person involved in the technical development of an AI system.
AI Researcher
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.
AI System
Represents a category of tasks or functions that an AI system can perform, such as sensory, knowledge, or control action processing.
AI System Capability
An action to achieve a specific goal using an AI model.
AI Task
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.
An organization or person responsible for reviewing the AI system for compliance.
Auditor
Activity of building system components and conducting integration testing.
Build and Integration Testing
A log of changes made to the system during its lifecycle.
Change Log
A record of a change, including reason, start and end date, stakeholders, and the change itself.
Change Record
Techniques that assign input data to predefined categories or classes.
Cloud service
The AI system is provided as a managed cloud service.
Techniques that group similar data points together based on their features.
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.
A computational resource used during the AI lifecycle, such as GPUs, CPUs, clusters or cloud instances.
Computational Resource
Methods that involve interactive learning through dialogue or feedback from users or other agents.
The activity of obtaining data from various sources.
Data Acquisition Activity
A procedure describing how data cleaning is performed, such as outlier detection and removal of invalid records.
Data Cleaning Procedure
Activity of capturing system events, performance, and traceability.
Data Monitoring and Logging
The pipeline for data-centric activities in the AI lifecycle.
Data Pipeline
Activity of combining, preprocessing data resulting in a different dataset.
Data Processing
Structured documentation for a dataset or training configuration, including provenance, scope and main characteristics.
Data Sheet
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.
Data Testing
Activity of using datasets for model training. Training data means data used for training an AI system through fitting its learnable parameters.
Data Training
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.
Data Validation
Activity of ensuring data quality through cleaning, filtering, and enrichment.
Data Wrangling/Cleaning
Represents a dataset used in the AI lifecycle.
Dataset
Approaches for selecting optimal actions under constraints or uncertainty.
A document declaring the system's conformity with standards, such as the EU Declaration of Conformity.
Declaration of Conformity
Activity of taking a model out of production.
Decommissioning
Activity of delivering the AI system to production environments.
Deployment
Techniques that reduce the number of features in the data while preserving important information.
Embedded component
The AI system is provided as a component embedded within a larger software or hardware product.
A specific feature designed to provide explanations for the AI system's output.
Explainable AI Feature
Activity of reviewing data requirements, sources, and initial assessments.
Exploration and Validation
Represents the hardware required for an AI system to run.
Hardware Component
Hardware product
The AI system is provided as, or integrated into, a physical hardware product.
A mechanism or measure to facilitate human intervention or supervision.
Human Oversight Mechanism
Methods that combine different learning paradigms, such as hybrid neuronal systems, learning with knowledge, and conversational learning.
Methods that combine neural architectures with symbolic reasoning or other AI techniques.
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.
Intended Purpose
The interface provided for users or operators of an AI system.
Interface
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.
Methods for structuring and organizing knowledge, including ontologies, semantic networks, and knowledge graphs.
An activity describing how data labels were assigned, including annotators, tools and guidelines.
Labeling Procedure
Techniques that integrate both symbolic or explicit knowledge representation and machine learning approaches.
A record of system events, performance, and traceability.
Log
Methods that use rules and principles for formal as well as interactive verification to identify patterns and logical structures and make logical deductions.
The pipeline for machine learning-centric activities in the AI lifecycle.
Machine Learning Pipeline
Methods that learn patterns from data, including supervised, unsupervised, semi-supervised, self-supervised, and reinforcement learning.
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.
Modality
Activity of defining architecture, selecting algorithms, and training the model.
Model Engineering
Activity of assessing performance, robustness, and accuracy.
Model Evaluation
Activity of preparing the trained model for deployment.
Model Packaging
Activity of managing revisions and tracking changes in model iterations.
Model Versioning
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.
Methods for improving solutions iteratively, including heuristic and metaheuristic approaches.
A metric used to evaluate the performance of an AI system.
Performance Metric
Approaches for autonomous and semi-autonomous planning procedures as well as plan recognition.
Activity of monitoring the system's performance and behavior after deployment.
Post-market Monitoring Activity
Activity of evaluating system performance in the post-market phase.
Post-market Performance Evaluation Activity
Techniques that handle uncertainty and incomplete information using probabilistic models, such as Bayesian networks and Markov decision processes.
Techniques that predict continuous output values based on input data.
Techniques that learn optimal actions through trial and error by receiving feedback in the form of rewards or penalties.
Techniques for exploring state spaces to find solutions.
Techniques that generate their own labels from the input data to learn useful representations.
Techniques that combine a small amount of labeled data with a large amount of unlabeled data during training.
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.
The pipeline for software development-centric activities in the AI lifecycle.
Software Code Pipeline
A logical or physical software component of the AI system, such as a service, module, or microservice participating in the overall processing.
Software Component
A software package or library that the AI system depends on.
Software Dependency
Activity of developing and writing software code for the AI system.
Software Development
Represents the concrete, executable software that realizes an algorithm.
Software Implementation
Software service / API
The AI system is provided as a hosted service consumed over an interface such as a web API.
Activity of managing revisions and tracking changes in software iterations.
Software Versioning
Standalone software
The AI system is provided as a standalone software application installed and run by the deployer.
A harmonised or other standard applied to the AI system.
Standard
Techniques that learn from labeled data to make predictions or classifications.
Methods that use knowledge representation and logic-based reasoning to model knowledge and make inferences.
Algorithmic strategies for search, optimization, planning and decision making
A data sheet focusing on training methodologies, techniques, hyperparameters and dataset usage for training, validation and testing.
Training Data Sheet
A feature or measure to ensure the transparency of an AI system.
Transparency Measure
Techniques that identify patterns and structures in unlabeled data.
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.
Visual Documentation
Links an activity to the artifact it affects or modifies.
affects
Tool or platform used to perform annotation (e.g., internal tool, external labeling platform).
annotation tool
Type of annotators involved in the labeling procedure (e.g., domain experts, laypersons, crowd workers).
annotator type
Links an AI system to a standard it has applied for compliance.
applies standard
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.
data characteristic
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.
data collection method
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.
data scope
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.
data selection criteria
Links a system or component to its software.
depends on
Description of what the visual documentation shows (e.g., 'external view', 'internal layout', 'marking placement').
depicts
The end date of the change.
end date
The methodology used to evaluate a performance metric.
evaluation method
Indicates that the output of one software component is used as input by another, forming part of the system's processing flow.
feeds into component
Links an AI Lifecycle Phase to a specific AI Activity.
has AI activity
Links a system or model to a description of its architecture.
has architecture
Relates an AISystem to an AISystemCapability it is designed to exhibit or support
has capability
Links an AI system to its change log.
has changelog
Links a ChangeLog to one or more ChangeRecords.
has change record
Links an AISystem to its constituent components like models and datasets.
has component
Links an AI system to its EU Declaration of Conformity document.
has declaration of conformity
Specifies the frequency or recurrence pattern at which an AI activity is performed.
has Frequency
Links an AI system to a mechanism for human oversight or intervention.
has human oversight
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.
has intended purpose
Links an AI system to the interface it provides to users or operators.
has interface
Links an AI system to its various phases throughout its entire lifecycle.
has lifecycle stage
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.
has modality
Links an evaluation activity to the performance metric it uses.
has performance metric
Links a software development activity to the developers responsible for it.
has responsible developer
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.
has risk
Links an AI system to its software components, such as services or modules that participate in the overall processing.
has software component
Links a DataPipeline activity to its source datasets.
has source dataset
Links the change record to stakeholders (airo:stakeholder or aidoc-ap:AIAgent).
has stakeholder
Links an AI system to measures in place to ensure transparency.
has transparency measure
Links an AI system to visual documentation such as photographs, diagrams, or illustrations showing external features, markings, and internal layout.
has visual documentation
Links an AIMethod to the AITask it is designed to perform.
implements task
Links an AI system to its deployer.
is deployed by
Links an AI system to its provider.
is provided by
Links an AI artifact to the location or entity where it is stored.
is stored at
Guidelines or instructions provided to annotators when assigning labels.
labeling guideline
Links a monitoring activity to the type of activity that is logged.
logs activity
Strategy for handling missing data (e.g., imputation, deletion, flagging).
missing data strategy
Methods used to identify and handle outliers or other problematic records in the data.
outlier handling method
Links a DataPipeline activity to the finished dataset it produces.
produces dataset
Links a monitoring activity to the log it produces.
produces log
Links a hardware component to the computational resource it provides.
provides computational resource
Data quality metrics or thresholds used to assess the dataset after cleaning.
quality metrics used
The reason for the change.
reason for change
Links the change record to the change (airo:Change).
records change
Links a system or artifact to the hardware components or servers it needs to run.
requires hardware
Configuration details of the computational resource (e.g., number and type of GPUs, CPU cores, memory, accelerator type).
resource configuration
Type of computational resource used (e.g., GPU cluster, CPU server, cloud instance).
resource type
Provider or vendor of the computational resource (e.g., cloud provider, hardware manufacturer).
resource vendor
The start date of the change.
start date
Links a storage location or entity to the AI artifacts it stores.
stores
Links a monitoring activity to where its logs are stored.
stores logs at
Indicates that an AITask contributes to achieving a broader AISystemCapability.
supports capability
Links an AI model to the specific AI method it is based on.
uses AI method
Links a testing activity to the dataset used for testing.
uses test data
Links a training activity to the dataset used for training.
uses training data
Links a validation activity to the dataset used for validation.
uses validation data
The version of the AI system or artifact.
version