Modeling AI systems' characteristics of energy consumption and their sustainability level as an extension of the FAIR data principles has been considered only rudimentarily. In this paper, we propose the Green AI Ontology for modeling the energy consumption and other environmental aspects of AI models. We evaluate our ontology based on competency questions. Our ontology is available at https://w3id.org/ Green-AI-Ontology and can be used in a variety of scenarios, ranging from comprehensive research data management to strategic controlling of institutions and environmental eforts in politics.
CEUR Workshop Proceedings
power draw
energy consumption
Identifier hasTeraFLOPS hasIdentifier hasPackagehPaoswEenreDrgraywConsumpthioansIdentifier hasFrequency hasEnergyConsumption hasPackagePowerDraw hasCores
CPU hasGPU hasCPU hasCPU hasCPUCount GPU hasGPU
utilization count
hasVersion name
hasUrl FPO hasFPO
haskWh kWh of electricity
hasLocation hasEnergyMixEmissionFactor hasGPUCount hasLocation subClassOf
usedEnergyMeasurementService usedEnergyMeasurementServiceAllExperiments hasEnergyMetrics hasEnergyMetricsAllExperiments
creates URL
hasProvider hasStorage
hasFLOPSperWatt hasSoftwareSettings hasHardwareSettings count count
hasVersion hasURL
hasName hasHardwareType hasMemory
OS hasOS
Programming hasModule language hasProgrammingLanguage #Paramters hasPrameters allExperminentsRunTime hasFinalRunTime hasModelSize
irao:hasAuthor irao:Researcher irao:Research
subClassOf subClassOf foaf:Person vivo:Project
hasWatt hasJoul
irao:hasPublication trainedOn hasResearchProjekt hasSocialCosthasKgOfCO2eq
irao:Informatics
subClassOf irao:Dataset social cost of carbon in US$
Kg of CO2eq irao:hasPublication
subClassOf irao:Publication irao:Software
In this paper, we propose – to our knowledge – the first ontology for modeling the energy
consumption of AI models. It is available at https://w3id.org/Green-AI-Ontology (OWL file at
https://w3id.org/Green-AI-Ontology/ontology). We create a knowledge graph based on our
ontology and evaluate our ontology based on competency questions. Our ontology can be used
in various scenarios, ranging from improved research data management to strategic controlling
of institutions and implementation of standards.
2. The Green AI Ontology
Ontology Design. Figure 1 shows the main classes and properties of the ontology. The
corresponding documentation is linked in our repository. Overall, our ontology is designed to
model the following aspects:
1. Metrics and tools: This part addresses the metrics that are used to measure the energy
consumption of AI models. Apart from the pure values (Energy Measure), we consider the
online services (Energy Measurement Service) with which the values can be determined.
Energy Measurement Service is defined as a subclass of Energy Measurement so that all
relevant key figures are modeled in addition to information about the service.
2. Hardware settings, cloud service, and location: The property hasHardwareSettings shows
information about the hardware used. In addition to the modeling of private
infrastructures, services/cloud providers are represented here. The location of the hardware (e.g.,
city, country), which may have an impact on the environmental balance, can also be taken
into account.
3. Software settings: The property hasSoftwareSettings shows information about the software
used, including software packages and modules.
4. Linking to scholarly linked data: This part of the ontology is designed to integrate the
modeled energy consumption information into the modeling of the scientific landscape.
Since the AI models are closely linked to further computer science artifacts (e.g., datasets,
software), we reuse the Ontology for Informatics Research Artefacts [
Knowledge Graph Construction. To create a knowledge graph based on our ontology,
we first applied 10 regex patterns (an extension of [
Thus, we refrained from this approach and instead asked AI researchers via a questionnaire to report the energy consumption of AI models published in papers. In this way, we obtained a proof-of-concept knowledge graph, modeling 40 AI models and 1,975 statements.
Ontology Evaluation. Following the best practices of ontology engineering (e.g., the NeOn ontology engineering methodology), we identified 15 competency questions (see our repository; based on 79 Green AI-related papers that are listed in our repository) that our ontology should be able to answer. Based on our created knowledge graph and created SPARQL queries (see our repository and Listing 1), we were able to answer all competence questions.
Use Cases. In the following, we outline several potential use cases of our ontology.
Research Data Management. The FAIR principles [
?AIModel gai:hasEnergyMetrics ?EnergyMetrics .
?EnergyMetrics gai:hasFPO ?FloatingPointOperations . } Listing 1: SPARQL query answering “How many floating point operations (FPO) do the AI models have?” an extension of these principles, allowing the modeling of usage information next to existing ontologies and knowledge graphs.
AI Systems. Engineers training and deploying AI models as well as end users may be
increasingly interested in knowing the environmental background of given AI models [
Society. From the perspective of popular sciences and politicians, our ontology complies with the rising public awareness of Green AI and environmental studies. The ontology enables energy consumption to be put into perspective (e.g., comparing energy consumption of language models and bitcoin mining).
In this paper, we proposed the Green AI Ontology for modeling the energy consumption of AI models. It can be used to extend academic knowledge graphs, to encourage researchers to provide information on the energy consumption of their AI models, and to ensure that the community appreciates this information.