=Paper=
{{Paper
|id=Vol-3254/paper361
|storemode=property
|title=The Green AI Ontology: An Ontology for Modeling the Energy Consumption of AI Models
|pdfUrl=https://ceur-ws.org/Vol-3254/paper361.pdf
|volume=Vol-3254
|authors=Michael Färber,David Lamprecht
|dblpUrl=https://dblp.org/rec/conf/semweb/0001L22
}}
==The Green AI Ontology: An Ontology for Modeling the Energy Consumption of AI Models==
https://ceur-ws.org/Vol-3254/paper361.pdf
The Green AI Ontology: An Ontology for Modeling
the Energy Consumption of AI Models
Michael Färber∗ , David Lamprecht
Karlsruhe Institute of Technology (KIT), Institute AIFB, Germany
Abstract
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 efforts in politics.
Keywords
Machine Learning, Green AI, Energy Consumption, Ontology Engineering
1. Introduction
Pre-trained language models such as GPT have been commended for their artificial general
intelligence capabilities and are nowadays widely used for tasks such as question answering,
information extraction, and text summarization. However, in the case of GPT-3 with its 175
billion parameters, the training required 10,000 GPUs and cost 552 metric tons of carbon
dioxide.1 Thus, the question arises of how “green” AI models are. Regardless of an ethical
assessment, we argue that it is useful to model AI systems’ characteristics of energy consumption
and sustainability (e.g., operating costs), extending the FAIR data principles [1], which focus
on the availability and reuse of research data and other artifacts. Existing ontologies and
knowledge graphs focus on the modeling of the research landscape, modeling publications,
authors, and venues (e.g., FaBiO, ORKG, MAKG) [2]. Furthermore, ontologies for modeling
software and neural networks have been proposed. For instance, the Ontology for Informatics
Research Artifacts (OIRA) [3] provides a way to model software and datasets. In FAIRnets [4],
the authors propose a schema for modeling neural networks. However, surprisingly, none of
these ontologies allow the modeling of the energy consumption of AI models (e.g., runtime or
CO2 footprint of pretrained language models, which can be measured via tools [5]).
21st International Semantic Web Conference, ISWC 2022, Virtual Event, October 23–27, 2022
∗
Corresponding author.
Envelope-Open michael.faerber@kit.edu (M. Färber); david.lamprecht@student.kit.edu (D. Lamprecht)
Orcid 0000-0001-5458-8645 (M. Färber); 0000-0002-9098-5389 (D. Lamprecht)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
CEUR Workshop Proceedings (CEUR-WS.org)
Proceedings
http://ceur-ws.org
ISSN 1613-0073
1
https://fortune.com/2021/04/21/ai-carbon-footprint-reduce-environmental-impact-of-tech-google-research-study/
Package Frequency in
energy Identifier Hz
power draw consumption
hasIdentifier Cores
count
hasPackagePowerDraw hasIdentifier hasFrequency
hasEnergyConsumption count
TeraFLOPS hasEnergyConsumption
hasCores Version
hasTeraFLOPS hasPackagePowerDraw
CPU Count
URL
GPU hasCPU
utilization
CPU hasVersion Name
Hardware Type
utilization hasURL
hasGPU
count
count
Memory hasName
hasCPUCount
GPU Count
hasGPU hasHardwareType
hasCPU Module/Package
Location
hasMemory
Programming
OS
hasLocation hasGPUCount hasModule language
hasEnergyMixEmissionFactor hasLocation
hasOS hasProgrammingLanguage
Provider/Cloud
Energy mix Service hasProvider Software Settings
emission factor Hardware Settings
hasStorage
Storage
hasSoftwareSettings #Paramters
Name hasFLOPSperWatt
Version
URL hasHardwareSettings
hasPrameters Time
hasVersion name FLOPS/W
hasUrl
allExperminentsRunTime
Energy
usedEnergyMeasurementService
Measurement AI Model hasFinalRunTime
usedEnergyMeasurementServiceAllExperiments Time
Service
hasEnergyMetrics hasModelSize
subClassOf hasEnergyMetricsAllExperiments
Model Size
creates
Energy Measure hasWatt Watt
FPO hasFPO trainedOn
irao:hasPublication
haskWh hasResearchProjekt
hasJoul
kWh of
hasSocialCost irao:Informatics
electricity hasKgOfCO2eq subClassOf irao:Dataset
Joul Research Artifact
irao:hasAuthor
social cost of irao:hasPublication
Kg of CO2eq
carbon in US$ subClassOf irao:Research
irao:Researcher
Projekt
irao:Publication
subClassOf subClassOf
irao:Software
foaf:Person vivo:Project
Figure 1: Main classes and properties of the Green AI Ontology.
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 infrastruc-
tures, 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 [3] considering best
practice for reusing existing ontologies. As a result, the modeled information is not a silo
but is closely linked to papers, data sets, and researchers. In this way, novel queries and
strategic controlling are possible (e.g., answering: What is the average energy consumption
of AI models developed and trained at my institution over the last five years? ).
Knowledge Graph Construction. To create a knowledge graph based on our ontology,
we first applied 10 regex patterns (an extension of [6]) on all 217,000 arXive computer science
papers as of July 31, 2020 (from http://unarxive.org). In this way, we obtained 3,016 energy
information units. However, we noticed that the accuracy of the matched patterns is insufficient
due to the low precision of the information extraction approach. For instance, a large portion
of the extracted energy information refers to non-AI models, such as mobile phones and e-cars.
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 [1] have been proposed to ensure that
resources are findable, accessible, interoperable, and reusable. Our ontology can be considered
SELECT * WHERE { ?AIModel a gai:AIModel .
?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 increas-
ingly interested in knowing the environmental background of given AI models [5] in order to
assess them more thoroughly than merely for their effectiveness. Our modeling of the energy
consumption of AI models is not restricted to one metric (e.g., CO2, run time); instead, our
ontology allows the modeling of several measurements for each AI model.
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).
3. Conclusion
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.
References
[1] M. D. Wilkinson, et al., The FAIR Guiding Principles for scientific data management and
stewardship, Scientific Data (2016) 2052–4463.
[2] V. B. Nguyen, V. Svátek, G. Rabby, Ó. Corcho, Ontologies Supporting Research-Related
Information Foraging Using Knowledge Graphs: Literature Survey and Holistic Model
Mapping, in: Proc. of EKAW, 2020, pp. 88–103.
[3] V. B. Nguyen, V. Svátek, Ontology for informatics research artifacts, in: Proceedings of the
18th Extended Semantic Web Conference, ESWC’21, 2021, pp. 126–130.
[4] A. Nguyen, T. Weller, M. Färber, Y. Sure-Vetter, Making neural networks FAIR, in: Pro-
ceedings of the Second Iberoamerican Conference and First Indo-American Conference on
Knowledge Graphs and Semantic Web, KGSWC’20, 2020, pp. 29–44.
[5] A. Lacoste, A. Luccioni, V. Schmidt, T. Dandres, Quantifying the Carbon Emissions of
Machine Learning, CoRR abs/1910.09700 (2019).
[6] P. Henderson, J. Hu, J. Romoff, E. Brunskill, D. Jurafsky, J. Pineau, Towards the systematic
reporting of the energy and carbon footprints of machine learning, CoRR abs/2002.05651
(2020).