Fazliev A.Z . , Kozodoev A.V., Privezetsev A.I.

Fazliev A.ZFazliev A.Z.., Kozodoev A.V., Privezetsev A.I., Kozodoev A.V., Privezetsev A.I. Institute of Atmospheric Optics SB RAS, Tomsk, RussiaInstitute of Atmospheric Optics SB RAS, Tomsk, Russia

Annotating theAnnotating the Information Resources Information Resources in the Distributed Information System in the Distributed Information System

on Molecular Spectroscopyon Molecular Spectroscopy

Annotating theAnnotating the Information Resources Information Resources in the Distributed Information System in the Distributed Information System

on Molecular Spectroscopyon Molecular Spectroscopy

The author would like to acknowledge the Russian Foundation for Basic Research for financial support

(grant 05-07- 90196)

Digital Libraries: Advanced Methods and Technologies. Digital Collections. Seventh National Russian Research Conference, Yaroslavl, October 04 - 06, 2005

The subject of atmospheric spectroscopyThe subject of atmospheric spectroscopy: : Molecule structure parametersMolecule structure parameters ((energy levelsenergy levels, , intermolecular potential intermolecular potential parametersparameters,…),…)Molecula spectraMolecula spectra ( (vibrationalvibrational, , rotationalrotational, , vibration-rotationalvibration-rotational) ) Spectral functionsSpectral functions ( (absorption coefficientabsorption coefficient, , transmittance functiontransmittance function, , absorption absorption cross-sectioncross-section, … ), … )

MMethodethodss of testing: of testing: experimental measurementsexperimental measurements, , quantum mechanical and quantum mechanical and semiempirical calculationssemiempirical calculations

Data levelData level:: 50 molecules are of interest for atmospheric research. Complete 50 molecules are of interest for atmospheric research. Complete data for the water molecule reflected in data for the water molecule reflected in 300 000 000 300 000 000 spectral linesspectral lines (99,5% - (99,5% - weak linesweak lines).).

Spectral data description levelSpectral data description level:: some line parameters are described by some line parameters are described by uuncertainty indices and bibliographyand bibliography. .

IntroductionIntroductionIntroductionIntroduction


HistoryHistory HistoryHistory

USA. Works are carried on from the late sixties. The databank HITRAN is created.

France. Works are carried on from the mid-seventies. The databank GEISA is created.

Russia. Works on informational resources for molecular spectroscopy are carried on from early eighties at the Institute of Atmospheric Optics SB RAS. The early nineties initiated the client side information systems. The advent of Internet technologies allowed development of a new type of information systems for the domain of molecular spectroscopy. The information resource (http://spectra.iao.ru) is based on the databanks Hitran and Geisa.


Molecular spectroscopy web sitesMolecular spectroscopy web sites

((BabikovBabikov YuYu..LL., ., Golovko V.FGolovko V.F., ., Mikhailenko S.NMikhailenko S.N.) (1999-2004).) (1999-2004)

Molecular spectroscopy web sitesMolecular spectroscopy web sites

((BabikovBabikov YuYu..LL., ., Golovko V.FGolovko V.F., ., Mikhailenko S.NMikhailenko S.N.) (1999-2004).) (1999-2004)

Spectroscopy of Atmospheric Gases http://spectra.iao.ru

Carbon Dioxide Spectroscopic Databank (http://cdsd.iao.ru)

Spectroscopy & molecular properties of Ozone (http://ozone.iao.ru)


Problems solved within these web sitesProblems solved within these web sitesProblems solved within these web sitesProblems solved within these web sites

1. 1. Spectroscopy of Atmospheric GasesSpectroscopy of Atmospheric Gases • Survey of the content of various datasources: HITRAN and GEISA spectral databanks, original

data obtained by IAO researchers in collaboration with other scientists, H2O spectra simulated by Partridge and Shwenke etc...

• Simulation of intensity diagram, absorption coefficient profile, transmittance, absorption, and radiance spectra at given conditions for selected molecule, isotopic species, and set of spectral bands or for selected wavenumber region and gas mixture.

• Spectra convolution with a given apparatus function. • Direct problem solution (spectrum simulation by given hamiltonian and dipole moment

parameters). • Gas and/or isotopic species mixture preparation by user. • Uploading of user spectra to server side and comparison of them to spectra obtained with the

system.

2. 2. Spectroscopy & Molecular Properties of OzoneSpectroscopy & Molecular Properties of Ozone • molecular structure and spectroscopic constants in the ground electronic state • potential function, dipole moment surface, transition moments • vibration and vibration-rotation energies and wavefunctions, isotopic effects • simulated and experimental spectra from MW to Infrared • Gas and/or isotopic species mixture preparation by user. • Uploading of user spectra to sever side and comparison of them to spectra obtained with the

system.

3. 3. Carbon Dioxide Spectroscopic DatabankCarbon Dioxide Spectroscopic Databank • Survey of the content of CDSD, HITRAN/HITEMP, GEISA spectral databanks for CO2 molecule



TechnologyTechnology

Organization

DB

Rough data

Public Public DBDB

Multimedia(video, sound, animation)

Metadata

e-Science

CalculationsCalculations

ExperimentExperiment

Information Resources of

e-Science


David De Roure, Nicholas Jennings, Nigel Shadbolt, A Future e-Science

Infrastructure, Report for EPSRC/DTI Core e-Science Programme, 2001.


The Data-Computation LayerThe Data-Computation Layer

The Information LayerThe Information Layer

The Knowledge The Knowledge LayerLayer

e-Science


System level

Middleware level

Application and interfaces level

Data and computation

Interface human-PC Interface PC-PC

Operating system, compilers

Database Management System

Hardware

Web server

Soft

ware Metadata service

Authorization, applied logic, linguistic support, dialog system facility, etc

Middleware core

IntroductionIntroductionPortal ATMOS. MiddlewarePortal ATMOS. Middleware

IntroductionIntroductionPortal ATMOS. MiddlewarePortal ATMOS. Middleware


The problems of molecular spectroscopyThe problems of molecular spectroscopyThe problems of molecular spectroscopyThe problems of molecular spectroscopy

Isolated molecule structureProblems: Finding of Watson’s Hamiltonian constants, parameters of short and long range

potential, wave functions and energy levels

Molecular spectral propertiesProblems: Finding of spectral line parameters (wave number, intensity, line width, line shift,

…). Identification of spectral lines from experimental spectra.

Spectral properties of atmospheric gasesProblems: Weak line study Continuum problem

Web site “Atmospheric spectroscopy” (http://saga.atmos.iao.ru)


Tomsk (IAO SB RAS)

Moscow

St.Petersburg

N.Novgorod

Data and metadata exchangeBasic host of DIS

Tomsk (TSU)

Metadata exchange

Client host

?

?

Distributed IS oriented for the problems of Distributed IS oriented for the problems of molecular spectroscopy (RFBR project)molecular spectroscopy (RFBR project)

Distributed IS oriented for the problems of Distributed IS oriented for the problems of molecular spectroscopy (RFBR project)molecular spectroscopy (RFBR project)


The Data-Computation LayerThe Data-Computation Layer The Data-Computation LayerThe Data-Computation Layer


Data groups in the distributed ICS Data groups in the distributed ICS ““Molecular spectroscopyMolecular spectroscopy""

1. Basic parameters of a molecule

These are the characteristics that determine molecular energy. Depending of description method they can be either full molecular Hamiltonian parameters (potential energy, dipole moment, etc.) or effective Hamiltonian parameters (rotational, centrifugal, and resonance constants, effective dipole moment parameters, etc.). Can add quadruple, octupole molecular moments and other parameters characterizing intermolecular interaction in gases.

2. Spectral line parameters- Parameters of isolated spectral line,- “Local” and “global” quanta indexes,- Collision dependent parameters.

3. Spectral functionsAbsorption coefficient, transmittance function, absorption cross-section, etc.


Absorption coefficient. Data structureAbsorption coefficient. Data structureAbsorption coefficient. Data structureAbsorption coefficient. Data structure

ExperimentSubstance Absorbing gas. Buffer gas.Thermodynamic conditions Temperature. Pressure. Partial pressure of buffer gas.Spectral parametersResolution. Path length. Frequency range.Absorption coefficientTransition frequency. Absorption coefficient.

CalculationSubstance Absorbing gas. Buffer gas.Thermodynamic conditions Temperature. Pressure. Partial pressure of buffer gas.Spectral parametersFrequency range. Contour type.Absorption coefficientTransition frequency. Absorption coefficient.

Data source Spectral line parameters. Statistical sums.


Basic concepts Basic concepts Basic concepts Basic concepts


Parameters of spectral linesParameters of spectral lines ((entityentity))

Isolated molecule- Vacuum wavenumber- Intensity- Lower level energy- Statistical weight of lower level- Identification

Interacting molecule (gas)- Line shift- Pressure induced linewidth (selfbroadening, buffer molecule

broadening)- Temperature dependence of linewidth

Other parameters- Reference indices- Uncertainty indices


The origin of transition frequency and intensity- experiment- calculation- synthetic

Intensity value scale- absolute- relative

Uncertainty - relative error- absolute error

Parameters of spectral linesParameters of spectral lines ((attributesattributes))


Last version ofLast version of metadatametadataLast version ofLast version of metadatametadata


Data source

Intensity

Low Energy Level

Transition frequency Origin

Upper and lower statistical weight

Error Type

Identification

Spectral line parameters of isolated molecule

Error TypeOrigin Value

Pressure Shift

Temperature Dependence (on Width)

Collisional Width Origin Error Type

Spectral line parameters of interactive molecule

Error TypeOrigin

Buffer Gas Ne, He, Ar, O2, H2, H2O, CO2, air, self, …

Spectral Line Parameters InputSpectral Line Parameters InputSpectral Line Parameters InputSpectral Line Parameters Input


Spectral Line Parameters InputSpectral Line Parameters InputSpectral Line Parameters InputSpectral Line Parameters Input


The process of input of the user data on spectral line parameters is based on substance classification and on the list of spectral line parameters. The general input processes are illustrated in Fig. 1.The data input process consists of several steps, where the user describes his data. This description supposes some standard metadata that refers to the resources and thermodynamic conditions the data is associated with. The user forms the structure to input the data and specifies the values for his selected attributes.

1. Watson’s Hamiltonian constants2. Energy levels and wave functions 3. Long range part of intermolecular potential (dipol, quadrupole, …, octopole

moments).4. Short range part of intermolecular potential (for example, Lennard-

Jones potential, - depth of potential well, σ - range of repulsive force)

Molecular structure dataMolecular structure dataMolecular structure dataMolecular structure data

Molecule classification:Class for Molecule (Linear triatomic molecules with large Fermi resonance , Non-linear triatomic molecules, Diatomic molecules, … )

Symmetry Group (C2v, Td, Cinf v, ... ) Group Classification (Asymmetric rotor, Spherical rotor, Doublet- П ground electronic states (half-integer J , integer F), … )

Data source Basic physical quantities


XML NotationXML NotationXML NotationXML NotationXML-SchemeXML-Scheme RDFRDF--SchemeScheme


Data entryData entry


Substance choiceSetting of thermodynamic and spectral parametersData source choice Setting of approximations for calculations

Absorption coefficient calculationAbsorption coefficient calculation


Absorption coefficient metadataAbsorption coefficient metadata


Data and metadata for machine processingData and metadata for machine processing

XML-document

RDF-documentDC-metadata


Web LanguagesWeb Languages Existing Web languages extended to facilitate content descriptionExisting Web languages extended to facilitate content description

• XMLXML XML Schema ( XML Schema (XMLSXMLS))• RDFRDF RDF Schema ( RDF Schema (RDFSRDFS))

XMLSXMLS is is notnot an ontology language an ontology language

RDFSRDFS isis recognisable as an ontology language recognisable as an ontology language

• ClassesClasses and and propertiesproperties• Sub/super-classesSub/super-classes (and properties) (and properties)• RangeRange and and domaindomain (of properties) (of properties)

OWLOWL is an ontology vocabulary is an ontology vocabulary• Well defined Well defined semanticssemantics• Formal propertiesFormal properties well understood (complexity, decidability) well understood (complexity, decidability)• Known Known reasoning algorithmsreasoning algorithms• Implemented systemsImplemented systems (highly optimised) (highly optimised)


The Information LayerThe Information Layer The Information LayerThe Information Layer


Metadata description methodsMetadata description methods

Register formationRegister formationCDF, RSS, AtomCDF, RSS, Atom

Formatted metadataFormatted metadata RDFRDF RequiredRequired - - DCDC Dependent on resource typeDependent on resource type

CIMI, MARCCIMI, MARC

Domain metadata RDFDomain metadata RDF based onbased on RDF-schemeRDF-scheme andand OWLOWL


Compound data sourcesCompound data sources

+

=

Tr.Freq. Line Intensity |R|2 Air HW Self HW T-depend. Pres. shift MoleculeIdentification

H2O00.2752 2.401e-30 3.48e-1 0.034 0 {v1v2v3 j ka kc}0.04

Data source 1


H2O0.2752 2.401e-30 0.041 0.0030 0 0.0035

Data source 2


H2O0.0030.2752 2.401e-30 3.48e-1 0.041 0.035 {v1v2v3 j kakc}0.04

DS 1=DS 2 DS 1=ds 2 DS 1 DS 2 DS 2 DS 1 DS 2 DS1=DS 2DS 1


Comparison of calculated absorption Comparison of calculated absorption coefficient of COcoefficient of CO22 with experimental values with experimental values


СО2-CO2

15 mkm Т=296 К Contour of Moskalenko 4.3 mkm Т=296 К 2397 - 2575 сm-1 Contour of Benedict

Т=296 К 2380 - 2585 cm-1 Contour of Boulet

Т=296 К 2400 - 2580 cm-1 Contour of Gal’tsev

Т=296 К 2140 - 2250 cm-1 Contour of Boulet, asymmetric

Т=296 К Contour of Moscalenko Т=218 К 2380 - 2585 cm-1 Contour of Boulet

Т=190-800 К 2400 - 2600 cm-1 Contour of Hartman 2.7 mkm Т=296 К 3750 – 4100 cm-1 Contour of Gal’tsev Т=296 К 3750 – 4100 cm-1 Contour of Bezard Т=296 К Contour of Moscalenko 2.3 mkm

Т=296 К 3800 – 4700 cm-1 Contour of Tonkov 2.0 mkm

Т=296 К Contour of Moscalenko 1.6 mkm

Т=296 К Contour of Moscalenko 1.4 mkm

Т=296 К 6985 – 7100 cm-1 Contour of Gal’tsev

Т=296 К Contour of Moscalenko

Absorption coefficientAbsorption coefficient. . What elseWhat else??

Line contour• Typical (Voigt, Doppler, Lorentz)

• Line wing theory (Tvorogov) (CO2, Н2О)

• Empirical contours СО2-CO2, Н2О-Н2О СО2-N2, СО2-O2, Н2О-Н2О + Н2О-N2


Problems with RDFSProblems with RDFS RDFS is RDFS is too weaktoo weak to describe resources in sufficient detail to describe resources in sufficient detail

• No No localised range and domainlocalised range and domain constraints constraints Can’t say that the range of Can’t say that the range of isDissociatedisDissociated is molecules is molecules

when applied to moleculeswhen applied to molecules• No No existence/cardinalityexistence/cardinality constraints constraints

Can’t say that all Can’t say that all triatomic moleculetriatomic moleculess have exactly 3 have exactly 3 atomsatoms

• No No transitive, inverse or symmetricaltransitive, inverse or symmetrical properties properties Can’t say that Can’t say that isPartOfisPartOf is a transitive property, that is a transitive property, that

hasPart is the inverse of hasPart is the inverse of isPartOfisPartOf or that or that come_into_collission_withcome_into_collission_with is symmetrical is symmetrical

• …… Difficult to provide Difficult to provide reasoning supportreasoning support


The Knowledge LayerThe Knowledge LayerThe Knowledge LayerThe Knowledge Layer“The aim of the knowledge layer is to act as an infrastructure to support the management and application of scientific knowledge to achieve particular types of goal and objective. In order to achieve this, it builds upon the services offered by the data-computation and information layers. The first thing to reiterate at this layer is the problem of the sheer scale of content we are dealing with. We recognise that the amount of data that the data grid is managing will be huge. By the time that data is equipped with meaning and turned into information we can expect order of magnitude reductions in the amount. However the amount of information remaining will certainly be enough to present us with a problem – a problem recognised as infosmog – the condition of having too much information to be able to take effective action or apply it in an appropriate fashion to a specific problem. Once information is delivered that is destined for a particular purpose, we are in the realm of the knowledge grid that is fundamentally concerned with abstracted and annotated content, with the management of scientific knowledge.”

David De Roure, Nicholas Jennings, Nigel Shadbolt, A Future e-Science Infrastructure, Report for EPSRC/DTI Core e-Science Programme, 2001.


Ontologies and Ontology RepresentationsOntologies and Ontology Representations

Most of the time we will just say “concept” and Most of the time we will just say “concept” and “ontology” but whenever anybody starts “ontology” but whenever anybody starts getting religious, remember…getting religious, remember…• It is only a representation!It is only a representation!

We are doing engineering, not philosophy – although We are doing engineering, not philosophy – although philosophy is an important guidephilosophy is an important guide

There is no one way!There is no one way!• But there are consequences to different waysBut there are consequences to different ways

and there are wrong waysand there are wrong ways• and better or worse ways for a given purposesand better or worse ways for a given purposes

• The test of an engineering artefact is whether it is fit The test of an engineering artefact is whether it is fit for purposefor purpose

Ontology representations are engineering artefactsOntology representations are engineering artefacts


Clash of intuitionsClash of intuitions• Subject Matter Experts motivated by custom & practiceSubject Matter Experts motivated by custom & practice

Prototypes & GeneralitiesPrototypes & Generalities• Logicians motivated by logic & computational tractability Logicians motivated by logic & computational tractability

Definitions and UniversalsDefinitions and Universals Transparency & predictability vs Transparency & predictability vs

Rigour & CompletenessRigour & Completeness

Conflation of ModelsConflation of Models• Meaning: Correctness of Classification & retrievalMeaning: Correctness of Classification & retrieval• Retrieval: Task of discovery, search, or findingRetrieval: Task of discovery, search, or finding• Use: Task of data entry, decision support, …Use: Task of data entry, decision support, …• Acquisition: Task of capturing knowledgeAcquisition: Task of capturing knowledge• Quality assurance: Criteria for whether it is ‘correct’Quality assurance: Criteria for whether it is ‘correct’

Why ontology is hardWhy ontology is hard


Special features of the ontology for Special features of the ontology for molecular spectroscopymolecular spectroscopy

Special features of the ontology for Special features of the ontology for molecular spectroscopymolecular spectroscopy

Presentation levelsPresentation levels::Physical modelPhysical model – – Mathematical modelMathematical model – – Information modelInformation model – – Program modelProgram model - …. - ….

Data Computation

Top- and bottom-level domain ontologiesTop- and bottom-level domain ontologies:: Quantum mechanics and electrodynamics (top level)Molecular spectroscopy Mathematical algorithms (bottom level)

Resource description in spectroscopyResource description in spectroscopy – – OWL DLOWL DL

Data sources:Experiment and calculation


Top-Level Categories (John Sowa)Top-Level Categories (John Sowa)http://www.jfsowa.com/ontology/toplevel.htmhttp://www.jfsowa.com/ontology/toplevel.htm


Thank you for your attentionThank you for your attention

The authors would like to acknowledge the experts in the domain of molecule spectroscopy

corresponding member of RAScorresponding member of RAS S.DS.D. . Tvorogov Tvorogov ProfProf. . A.D. Bykov (IAO SB RAS, Tomsk)A.D. Bykov (IAO SB RAS, Tomsk)

ProfProf. . O.B. Rodimova (IAO SB RAS, Tomsk) O.B. Rodimova (IAO SB RAS, Tomsk) Prof. N.N.Fillipov (SPbU, S.Petersburg) Prof. N.N.Fillipov (SPbU, S.Petersburg) Prof. M.V.Tonkov (SPbU, S.Petersburg)Prof. M.V.Tonkov (SPbU, S.Petersburg)

Prof. J.Tennyson (UCL, London)Prof. J.Tennyson (UCL, London)Dr. M.Yu.Tret’yakov (IAP RAS, N.Novgorod)Dr. M.Yu.Tret’yakov (IAP RAS, N.Novgorod)Dr. O.V. Naumenko (IAO SB RAS, Tomsk)Dr. O.V. Naumenko (IAO SB RAS, Tomsk)


Fazliev A.Z . , Kozodoev A.V., Privezetsev A.I.

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