OntoKin: An Ontology for ChemicalKinetic Reaction Mechanisms

Preprint Cambridge Centre for Computational Chemical Engineering ISSN 1473 – 4273

OntoKin: An Ontology for ChemicalKinetic Reaction Mechanisms

Feroz Farazi1, Jethro Akroyd1,4, Sebastian Mosbach1,4, Philipp Buerger1,

Daniel Nurkowski2, Markus Kraft1,2,4

released: 18 January 2019

1 Department of Chemical Engineeringand BiotechnologyUniversity of CambridgePhilippa Fawcett DriveCambridge, CB3 0ASUnited KingdomEmail: mk306@cam.ac.uk

2 CMCL InnovationsSheraton HouseCambridge, CB3 0AXUnited Kingdom

3 School of Chemicaland Biomedical EngineeringNanyang Technological University62 Nanyang DriveSingapore 637459

4 Cambridge Centre for Carbon Reductionin Chemical Technologies (CARES C4T)#05-05 CREATE Tower1 CREATE WaySingapore 138602

Preprint No. 218

Keywords: OntoKin ontology, semantics, Chemical Semantic Web,chemical data, knowledge base, chemical kinetic reaction mechanism

Edited by

CoMoGROUP

Computational Modelling GroupDepartment of Chemical Engineering and BiotechnologyUniversity of CambridgePhilippa Fawcett DriveCambridge CB3 0ASUnited Kingdom

E-Mail: c4e@cam.ac.ukWorld Wide Web:http://como.ceb.cam.ac.uk/

Abstract

An ontology for capturing both data and the semantics of chemical kinetic reactionmechanisms has been developed. Such mechanisms can be applied to simulate andunderstand the behaviour of chemical processes, for example, the emission of pol-lutants from internal combustion engines. An ontology development methodologywas used to help produce the semantic model of the mechanisms, and a tool was de-veloped to automate the assertion process. As part of the development methodology,the ontology is formally represented using OWL, assessed by domain experts andvalidated by applying a reasoning tool. The resulting ontology, termed OntoKin, hasbeen used to represent example mechanisms from the literature. OntoKin and its in-stantiations are integrated to create a Knowledge Base (KB), which is deployed usingthe RDF4J triple store. The use of the OntoKin ontology and the KB is demonstratedfor three use cases - querying across mechanisms, modelling atmospheric pollutiondispersion and a mechanism browser tool.

Develop

Ontologists Domain Experts Programmers

Protégé OWL API HermiT OntoKin

Ontology(TBox)

KnowledgeSources

Ontological ABoxes

ABoxGenerator

KB

RDF4J Server

Reaction Mechanisms

Mechanism Viewer Use Case

Querying AcrossMechanisms

Use Case

SRM-ADMS Simulation Use Case

SPARQL Query

Engine Behaviour

Highlights

• The OntoKin ontology has been developed to represent chemical kinetic mech-anisms.

• An ABox Manager has been developed to convert mechanisms from CHEMKINformat to OWL.

• A Knowledge Base including representations of mechanisms using OWL hasbeen created.

1

Contents

1 Introduction 3

2 J-Park Simulator (JPS) 4

3 Ontology development methodology 5

4 The OntoKin ontology 6

4.1 OWL representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

5 Population of the knowledge base 8

6 Use cases 9

6.1 Querying across reaction mechanisms . . . . . . . . . . . . . . . . . . . 9

6.2 SRM-ADMS meets the Semantic Web . . . . . . . . . . . . . . . . . . . 11

6.3 Importing and exporting OWL/RDF data using a Mechanism Viewer . . . 12

7 Conclusions 12

References 14

2

1 Introduction

The Semantic Web [1] offers the ability to attach meaning to data and represent extractedknowledge from data. Ontologies [10] are the core means to represent data, its meaningand knowledge about data on the Semantic Web. Agents [15] can use the Semantic Webeasily to find ontologies to assist (human and non-human) users. Having been influencedby the Semantic Web, many people have taken the initiative to build a Chemical SemanticWeb [12, 13] using chemical ontologies [5, 35] to meet an increasing interest to generateknowledge from chemical data and to facilitate data sharing [21].

A number of ontologies have been developed to capture and represent the semantics andknowledge of chemicals and chemical interactions with different levels of granularity. TheChemicals ontology [5] was developed to represent elements and chemical substances.Sankar and Aghila [34] applied the same methodology used to develop the Chemicals on-tology to build an ontology for organic reactions, and developed the Chemical OntologicalSupport System (COSS) to retrieve instances of different types of organic reactions usinga semantic search. Hastings et al. [13] created an ontology represented in OWL (WebOntology Language) to classify chemical compounds based on their structure. Marquardtand co-workers developed OntoCAPE (Ontology for Computer Aided Process Modelling)as a formal ontology for modelling chemical process engineering [27], including the con-cepts of elements, species, molecular entities and reactions.

In addition, a number of cross-domain ontologies that cover aspects of chemical mod-elling have been developed. ChEBI [3] is an ontology created for representing conceptsand relations (object properties) belonging to chemistry and biology. ChEBI supports thestructural description of ‘small’ molecules. Hill et al. [16] combined ChEBI and the GeneOntology to develop the BioChEBI ontology to integrate chemical and biological descrip-tions of gene products. CompChem [32] was developed as an RDF-based representationof the semantics of computational chemistry calculations, whilst the Gainesville Core on-tology [37] allows the description of the geometric structure of molecules. PubChemRDF[7] represents structures and metadata of chemical substances and compounds. In addi-tion to chemical semantic resources, there are initiatives that have led to well-establishedchemical databases (PubChem [20], PrIMe [6] and Reaxys1 - to name a few).

A chemical kinetic reaction mechanism (also termed mechanism or reaction mechanism)is a fundamental part of simulations to investigate the behaviour of chemical processes.For example, the emission of pollutants from combustion processes. The J-Park Simulator(JPS)2 is an example of an intelligent application toolset that, following ideas discussedin [21], uses semantic representation to harnesses the reasoning and inferencing powerof ontologies to perform cross-domain simulations. We can apply ideas related to theChemical Semantic Web to address the needs of the combustion community, where thereis an interest in understanding how the chemistry of a fuel affects its performance. Mech-anisms for fuels can be very complex, containing thousands of species and reactions.One challenge facing the community is the inconsistency between different models of thesame fuel, where models have been developed for different situations and tested againstdifferent data. Ideally, such models should be consistent and universally applicable.

1 https://www.reaxys.com2 http://www.theworldavatar.com

3

The purpose of this paper is to take steps towards addressing these issues by developingan ontology to represent chemical kinetic reaction mechanisms. Whenever possible, wereuse concepts and object properties from existing ontologies, in particular OntoCAPE.The paper makes the following contributions:

• The development of OntoKin, an ontology for representing mechanisms.

• An ABox Manager tool to convert mechanisms from CHEMKIN format to OWL,and vice versa.3

• A Knowledge Base (KB), which includes OntoKin and representations of mecha-nisms using OWL.

The paper is structured as follows. Section 2 gives an overview of JPS, which is thecontext of this work. Section 3 describes the development methodology. Section 4 de-scribes OntoKin. Section 5 shows how the KB was populated with mechanisms. Section6 presents some use cases to demonstrate the application of OntoKin. Conclusions arediscussed in Section 7.

2 J-Park Simulator (JPS)

JPS is a cross-domain simulation platform built on Semantic Web technologies. Modelsand data are represented in ontological form, the instances of which form a knowledgegraph. JPS includes computational agents that act on the knowledge graph, for exampleby adding data acquired either via sensors or by running computational models.

JPS has been applied to many aspects of Industry 4.0. One example concerns the Eco-Industrial Park (EIP) on Jurong Island in Singapore. An EIP is a group of product manu-facturers and service providers working together based on exchange relations to addressissues related to pollution and reuse (of waste materials) to achieve environmental andeconomic benefit [24]. In an EIP, the geographical vicinity of enterprises forming thegroup is important for cost efficiency and environmental footprint reduction [14]. EIPsmay include material exchanges, energy systems and wastewater treatment networks,which can be modelled at different levels such as unit operations, processes, plants andnetworks as well as optimised for improved performance [18, 31].

By design, each tool in the JPS toolset can work independently and as part of a systemconsisting of multiple tools. In order to allow tools and systems to communicate with eachother, interoperability was emphasised from the early stage of the JPS development. Inorder to facilitate interoperability, the representation of the semantics of data and modelswas adopted as an underlying technique [30]. In the context of JPS, ontologies includingOntoEIP, the EIP energy system ontology and the biodiesel plant ontology have beendeveloped by extending the relevant branches of OntoCAPE [23]. OntoEIP is an ontologydesigned for creating a knowledge base with information originating from resource and

3 CHEMKIN-III [2, 19], and its predecessors and successors, defined a de facto standard file format for mechanisms in thecombustion community.

4

transportation networks, chemical process systems and plants for managing an EIP [40].The EIP energy system ontology was developed for building a decision-making systemby integrating data from heterogeneous sources [38]. The biodiesel plant ontology wasbuilt for creating a knowledge base with information about the process simulation andoptimisation of biodiesel production [39].

One application of interest is to couple simulations of emissions from internal combustionengines to simulations of the atmospheric dispersion of these emissions. This requires achemical model (i.e. a reaction mechanism) for the fuel used by the engine to be repre-sented as part of the knowledge graph. The work described in this paper is positionedwithin this context. It addresses the needs of JPS by developing an ontology to representchemical mechanisms and a KB to store corresponding mechanism data. This will supportthe automation of processes within JPS by enabling the introduction of intelligent agentsto query the KB to search for, find and extract mechanisms for a given task.

3 Ontology development methodology

An iterative approach was used to develop the ontology. This allowed learning fromprevious iterations to be incorporated into the development process to improve the qualityof the ontology. At each iteration, three sequential top level activities were performed –preparation, creation and/or modification and quality assessment – to accomplish a macrotask. For example, to extend the ontology to include a defined set of additional conceptsand object properties.

• Preparation: Decide the subtasks that will be executed in the current iteration, thelevel of expertise required for the creation and quality assessment, and who shouldbe involved for the successful accomplishment of the macro task.

• Creation and/or Modification: Create and/or modify the ontological TBox [22]consisting of concepts, relations between them and properties, possibly with as-signment of their domains and ranges.

• Quality assessment: Assess the quality of the completed subtasks to identify oppor-tunities to improve other areas of the TBox.

These activities follow the ontology development approaches proposed by Giunchigliaet al. [8, 9], Grüninger and Fox [11], Noy and McGuinness [28], Pinto and Martins [33],Uschold and King [36] and Fernandez-Lopez et al. [4].

The preparation task is a sequential combination of understanding the purpose [33, 36]and motivating scenario [11], identifying competency questions [11, 28] and definingthe scope [28, 33]. The creation and/or modification task is a sequential combinationof concept and property identification [9, 36], analysis [9], synthesis [9], standardisation[8, 9] and formalisation [4, 9, 33]. Quality assessment consists of evaluation [4, 33] andvalidation [4]. Documentation [4, 33, 36] should be performed throughout.

5

4 The OntoKin ontology

The OntoKin ontology, published at http://www.theworldavatar.com/ontology/ontokin/OntoKin.owl, can represent mechanisms consisting of a set of chemical reac-tions occurring between species. A species is composed of elements (for example, wateris composed of hydrogen and oxygen) and may exist in different phases (for example,water may exist in a gas phase or may be adsorbed on some surface phase). With respectto a given reaction, the reactants are the set of species consumed by the reaction and theproducts are the set of species produced by the reaction. The ontology can represent therelations between these concepts as well as data and metadata about instances of them.

An example reaction is 2H2 + 1O2 −−→ 2H2O. H2, O2 and H2O are species, of whichH2 and O2 are reactants and H2O is a product. The elements contained within this setof species are H and O. The relation between a chemical entity and a reaction can beexpressed as: a reaction having a reactant (O2), which is composed of a species (O2) anda number (1) called a stoichiometric coefficient, expressing the amount of the speciesconsumed by the reaction. Following are some examples of data: the reaction rate (todescribe the speed of a reaction) and thermodynamic data (to describe the enthalpy, heatcapacity and entropy of a species as a function of temperature and pressure). Metadatacan be exemplified as the creator of the mechanism and bibliographic data about where itwas published.

In order to develop the OntoKin ontology, concepts and properties were extracted fromdomain experts and published resources. Notable resources include research papers, tech-nical reports [for example, 2, 19, 25] and chemical process knowledge. This involvedsignificant contributions and input from domain experts. As recommended by the domainexperts, approximately 20 mechanisms were studied thoroughly. The mechanisms werechosen to have good coverage of different types of species and reactions. The technicalreports provided detailed mathematical descriptions of the thermodynamic model (thermomodel from now on), transport model and rate coefficient models. In the end, 57 atomicconcepts and 35 object properties were identified, of which a subset is shown in Figure 1.

The ontology is conceptually divided into 5 modules, each of which is described below.In the description, the rdfs:subClassOf relation is represented as is-a.

• Reaction Mechanism: A reaction mechanism refers to the chemistry in a chemicalkinetic model, for example to model the combustion of hydrogen. The reactionmechanism module is linked to the phase module to model the fact that a reactionmechanism contains a gas phase and may contain any number of (solid) materials.A relation is established between each of these concepts and the reaction mechanismusing the containedIn object property.

• Phase: A phase is “a distinct and homogeneous form of matter (i.e. a particularsolid, liquid, or gas) separated by its surface from other forms” [29]. The phasemodule includes gas phase, site phase and bulk phase as subclasses of phase, wheresite and bulk phases exist as part of a material. Note that the ontological modelling(or representation) of liquid phases is beyond the scope of the current work.

• Chemical Reaction: A chemical reaction is a process that converts chemical en-

6

ReactionMechanism*

Phase

containedIn  exactly 1

Material

GasPhase SitePhase BulkPhase

ChemicalReaction*

GasPhaseReaction SurfaceReaction

is-a

Element

belongsToMaterialonly

belongsToPhaseonly

is-a existsIn only

Reactant Product

StoichiometricCoefficient*

hasReactant* only 

hasProduct* only

hasStoichiometricCoefficient*only

indicates MultiplicityOf*

only 

Species orMolecular

Entity*

ElementNumberhasElementNumber only

indicatesNumberOf only ReactionOrder hasReactionOrderonly

ForwardReaction

ReverseReaction

ThermoModel*

TransportModel

hasThermoModel

hasTransportModel

ArrheniusCoefficient hasArrheniusCoefficient only

LandauTellerReaction

ArrheniusReaction

LandauTellerRateCoefficient hasLandauTellerRateCoefficient only

participatesIn only

participatesIn

StickingCoefficient

CoverageDependency hasCoverageDependency only

hasStickingCoefficient only

Reaction Mechanism Chemical Reaction

Phase

Species

Rate Coefficients

is-a

RateCoefficient

hasElement only

SurfaceCoverageReaction

is-a

StickingCoefficientReaction

FallOffReaction FallOffModelCoefficient hasFallOffModelCoefficient only

Rea

ctio

n ra

te m

odel

sSp

ecie

s-ce

ntric

con

cept

s an

d re

latio

ns

is-a

is-a

is-a

belongsToPhaseonly

is-a

is-a

appliesTo only

* Denotes concepts and relations defined by OntoCAPE.

Figure 1: The core concepts and properties of the OntoKin ontology.

tities called reactants into chemical entities called products [23]. In the ontology,both the reactants and products are represented as species or molecular entities. Theontology models gas-phase reactions, where the reactants and products exclusivelybelong to the gas phase, and surface reactions, where the reactants and productsbelong to a combination of the gas phase and a material. The ontology modelseach reaction in terms of a forward reaction and a reverse reaction to allow for thepossibility that the reaction is reversible, i.e. that it may convert both reactants toproducts and products to reactants.

The multiplicity (or number) of each reactant and product entity participating in achemical reaction is modelled using a stoichiometric coefficient. The order of reac-tion with respect to each reactant participating in the forward reaction and each

7

product participating in the reverse reaction (if one exists) is modelled using areaction order. 4

The ontology includes a number of common reaction rate models, including Ar-rhenius, Landau-Teller and fall-off models for gas-phase reactions, and coverage-dependent and sticking coefficient models for surface reactions.

• Rate Coefficients: The rate coefficients are used to evaluate the rate constant thatappears in a reaction rate model. The details of the coefficients that are requireddepends on the choice of rate model.

• Species: A species can be defined as “an ensemble of chemically identical molec-ular entities” [26]. The elemental composition of a species or molecular entity ismodelled using an element number to describe the multiplicity of each chemicalelement appearing in the species.

Each species or molecular entity may be associated with a thermo model describingits thermodynamic properties (for example, enthalpy, heat capacity and entropy)and a transport model describing its transport properties (for example, viscosity,thermal conductivity and diffusion coefficients).

4.1 OWL representation

Concepts and is-a relations between concepts are represented using the owl:Class andrdfs:subClassOf constructs, respectively. Object properties are represented using theowl:ObjectProperty. The equivalence (≡), intersection (u) and union (t) relations anduniversal quantification (∀) are represented using owl:equivalentClass, owl:intersectionOf,owl:unionOf and owl:allValuesFrom, respectively. In terms of expressivity and reasoning,the ontological representation of OntoKin is valid in OWL 1 and OWL 2, such that rea-soners compatible with DL SROIQ [17] can respond to reasoning and inference-basedqueries.

5 Population of the knowledge base

A preliminary knowledge base (KB) by integrating OntoKin with ontological representa-tion of 34 publicly available mechanisms obtained from research organisations includinguniversities and scientific laboratories. The largest mechanism contained approximately3,000 species and 19,000 chemical reactions. The smallest mechanism, on the other hand,contained as few as 14 species and 33 reactions. The mechanisms can be analysed fromthe perspective of the level of granularity and the parametric conditions. The level ofgranularity relates to whether it is a detailed mechanism that attempts to describe the fullchemistry of a system, or a reduced mechanism that attempts to mimic the behaviour ofthe full chemistry under a restricted set of conditions. The parametric conditions relate to

4 The order of reaction with respect to an entity is the exponent to which the term for the entity is raised in the reaction rateequation.

8

whether different mechanisms describe things that purport to describe the same physicsin the same way.

Figure 2 shows the toolset used to populate the KB. The toolset includes an ABox Managerthat uses the OntoKin TBox and OWL API5 to convert CHEMKIN [2, 19] mechanism filesto OntoKin ABoxes, and a KB Generator that imports the OntoKin TBox and ABox(es)into a KB. The KB was deployed using the RDF4J6 triple store. The toolset supportsrunning the conversion and import independently as batch processes.

The ABox Manager was developed using CHEMKIN mechanisms to provide examplesof the mapping to the ontology. CHEMKIN mechanisms can consist of up to four files.The main file specifies the chemical elements, the species and reactions that belong tothe gas phase, and optionally, thermodynamic data for species in this file. A second filespecifies any materials, any site and bulk phases that belong to a material, any species thatbelong to these phases, any thermodynamic data for these species, and any reactions thatbelong to a material. These files may be supplemented by thermodynamic and transportdata files. These provide data for any species for which thermodynamic and transportdata have not been defined. In the current work, the thermodynamic data is in the formof 7-coefficient NASA polynomials [25]. Bidirectional conversion was used to provethat the ABox Manager faithfully preserved the source data, and facilitates backwardcompatibility.

6 Use cases

This section introduces some use cases to show how Semantic Web technologies such asthe OntoKin ontology and knowledge base can add value. The use cases show examplesof how mechanisms can be queried, downloaded and employed for modelling.

6.1 Querying across reaction mechanisms

The development of the KB allows queries and reasoning to be performed across mech-anisms. The domain experts were consulted to identify the types of queries of interest.The questions ranged from being as simple as requesting all the mechanisms containing aspecific species or from a specific source to much more complex questions. For example,requesting all mechanisms which have inconsistent rate models for the same reaction. Inthis case sameness is defined in terms of the equality of the reactants and products, butin general, the definition of sameness could be more extensive. The most appropriatedefinition for a given scenario is considered to be an open question.

A mechanism query tool was built on top of the KB. It reuses the RDF4J SPARQL End-point, which functions as the query interface and allows users to search for informationvia computers and handheld devices. The purpose of the tool is to support users suchas combustion scientists and modellers in their daily activities. Future developments of

5 https://github.com/owlcs/owlapi6 http://rdf4j.org

9

ReactionMechanism

ReactionMechanism

ReactionMechanism

KB

RDF4J Server

Web Server

KB GeneratorABoxManager

ABoxes

OntoKinOntology(TBox)

ReactionMechanism

Figure 2: The toolset for generating the OntoKin KB.

the knowledge base and query tool will consider how to link imported mechanisms withother data sources, for example experimental data and/or ab initio calculations of thermo-dynamic data.

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6.2 SRM-ADMS meets the Semantic Web

Figure 3 shows a cross-domain use case from JPS7. In this example, JPS uses the SRMEngine Suite8, a toolset developed to model the performance of and emissions from inter-nal combustion engines, to estimate the exhaust emissions from fixed diesel generators.JPS then uses ADMS9, the Atmospheric Dispersion Modelling System, to simulate thedispersion of these emissions in the vicinity of each generator. The SRM simulations usechemical mechanisms obtained via the OntoKin KB. The ADMS simulations use weatherdata queried from the World Wide Web. The distribution of the emissions is visualised inJPS as an overlay on a Google Map.

Weather data

SRM Engine Suite Simulation

ADMSSimulation

Visualisation in JPS

Web Server Web

Performance and emissions

Pollutantdispersion

KB

RDF4J Server

Figure 3: The SRM-ADMS use case.

An API has been developed using the RDF4J Model API10. The API takes advantageof the choice to represent mechanisms using ontologies (in any of the Semantic Webformats including OWL and RDF). The API enables the SRM to programmatically importmechanisms by sending a request with the IRI of the mechanism to the KB. The KBresponds by returning the RDF representation of the mechanism, which can be convertedto a form that is readable by the SRM.

7 http://www.theworldavatar.com/JPS/?lat=52.076&lon=4.31&zoom=14.5&tilt=0.0&rotation=0.68 https://cmclinnovations.com/products/srm9 https://cerc.co.uk/environmental-software.html

10 http://docs.rdf4j.org/programming

11

6.3 Importing and exporting OWL/RDF data using a MechanismViewer

The Mechanism Viewer, originally developed as part of the SRM Engine Suite, is a toolto visualise chemical mechanisms, including the elements, species, thermodynamic andtransport data, reactions and rate parameters belonging to the mechanism. The viewer hasbeen extended using the API developed for the SRM-ADMS use case to import and exportmechanisms in OWL and RDF formats, and to browse, query and import mechanismsfrom the KB.

In this manner, the Mechanism Viewer is one tool that can be used to facilitate commu-nity contribution to the growth of the Semantic Web by providing an easy way to query,retrieve and distribute mechanisms. In the future it is proposed to develop tools to sup-port more advanced interactions with the KB. For example, to compare data betweenmechanisms, to link to other data sources, to assess the quality of the data, to find andresolve inconsistencies and inaccuracies and to retrieve consistent mechanisms using thebest quality data for a given system.

7 Conclusions

This paper has presented an ontological model to capture the semantics of chemical ki-netic reaction mechanisms, with a particular focus on combustion chemistry. The ontol-ogy was developed using an iterative method that divided the development into smallermacro tasks, with each task covering one aspect of the ontology in a given iteration.Learning was carried forward between iterations, contributing to the overall quality of theontology.

An ABox Manager tool was developed to convert mechanisms from CHEMKIN format toOWL, and vice versa. Bidirectional conversion was used to ensure full preservation of thesource information and facilitates backward compatibility. A KB containing mechanismsfrom the literature has been developed and deployed using an RDF4J triple store. The useof the knowledge base was demonstrated for example use cases, ranging from respond-ing to queries to simulation of the atmospheric dispersion of pollutants from combustionprocesses.

It is hoped that the tools developed in this paper will provide a first step to providingan easy way to query, retrieve and distribute mechanisms via the Semantic Web. In thefuture it is proposed to develop tools to support more advanced community involvement,including linking to other data sources and identifying the best quality data for a givensystem.

Supplementary material

An archived version of the OntoKin ontology is available via the University of Cambridgedata repository at https://doi.org/10.17863/CAM.35439 and the living version of

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the ontology is available via the JPS website at http://www.theworldavatar.com/ontology/ontokin/OntoKin.owl.

Acknowledgements

This work was partly funded by the National Research Foundation (NRF), Prime Min-ister’s Office, Singapore under its Campus for Research Excellence and TechnologicalEnterprise (CREATE) programme, and by the European Union Horizon 2020 Researchand Innovation Programme under grant agreement 646121.

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