- 1. Dr. Barry SmithDirectorNational Center for Ontological
Researchhttp://x.co/qtYqTowards Joint Doctrine for
MilitaryInformatics1
2. Barry Smith who am I?Ontology work forNextGen (Next
Generation) Air Transportation SystemNational Nuclear Security
Administration, DoEJoint-Forces Command Joint Warfighting
CenterArmy Net-Centric Data Strategy Center of ExcellenceArmy
Intelligence and Information Warfare Directorate(I2WD)and for many
national and international biomedicalresearch and healthcare
agencies2 3. The problem of Big Data in biomedicine:Multiple kinds
of data in multiple kinds of silosLab / pathology dataElectronic
Health Record dataClinical trial dataPatient historiesMedical
imagingMicroarray dataProtein chip dataFlow cytometryMass
specGenotype / SNP dataeach lab, each hospital, each agency has its
ownterminology for describing this data 3 4. How to find your
data?How to reason with data when you find it?How to understand the
significance of the datayou collected 3 years earlier?How to
integrate with other peoples data?Part of the solution must involve
consensus-based, standardized terminologies and codingschemes4 5.
In the olden dayspeople measured lengths using inches,
ulnas,perches, kings feet, Swiss feet, leagues of Paris,etc., etc.5
6. On June 22, 1799, in Paris,everything changed6 7. International
System of Units7 8. Making data (re-)usable throughstandard
terminologies Standards provide common structure and terminology
single data source for review (less redundantdata) Standards allow
use of common tools and techniques common training single
validation of data8 9. Unifying goal: integration of biologicaland
clinical data within and across domains across different species
across levels of granularity (organ,organism, cell, molecule)
across different perspectives (physical,biological, clinical)9 10.
One successful part of the solution tothis problem =
Ontologiescontrolled vocabularies (nomenclatures)plus definitions
of terms in a logical language10 11. 11 12. 12types vs. instances
13. 13names of instances 14. 14names of types 15. Ontologies are
computer-tractable representations oftypes in specific areas of
reality are more and less general (upper and lowerontologies) upper
= organizing ontologies lower = domain ontologies15 16.
FMAPleuralCavityInterlobarrecessMesotheliumof PleuraPleura(Wallof
Sac)VisceralPleuraPleural SacParietalPleuraAnatomical
SpaceOrganCavitySerous SacCavityAnatomicalStructureOrganSerous
SacMediastinalPleuraTissueOrgan
PartOrganSubdivisionOrganComponentOrgan CavitySubdivisionSerous
SacCavitySubdivisionFoundational Model of Anatomy16 17.
17ontologies = standardized labelsdesigned for use in annotationsto
make the data cognitivelyaccessible to human beingsand
algorithmically accessibleto computers 18. by allowing grouping of
annotationsbrain 20hindbrain 15rhombomere 10Query brain without
ontology 20Query brain with ontology 4518Ontologies facilitate
retrieval of data 19. 19ontologies = high quality
controlledstructured vocabularies used for theannotation
(description, tagging) ofdata, images, emails, documents, 20. The
problem of retrieval, integrationand analysis of siloed data is not
confined to biomedicine affects every domain due to massive legacy
ofnon-interoperable data models and datasystems and as new systems
are created along thesame lines, the situation is constantly
gettingworse.20 21. The problem: many, many silos DoD spends more
than $6B annually developing aportfolio of more than 2,000 business
systemsand Web services these systems are poorly integrated deliver
redundant capabilities, make data hard to access, foster error and
waste prevent secondary uses of datahttps://ditpr.dod.mil/ Based on
FY11 Defense Information TechnologyRepository (DITPR) data21 22.
Some questions How to find data? How to understand data when you
find it? How to use data when you find it? How to compare and
integrate with other data? How to avoid data silos?22 23. Favored
solution: ber-model (NIEM,JC3IEDM ) must be built en bloc
beforehand inflexible, unresponsive to warfighter needs heavy-duty
manual effort for both constructionand ingestion, with loss and/or
distortion ofsource data and data-semantics might help with data
retrieval and integration but offers limited analytic capability
has a limited lifespan because it rests on one pointof view23 24.
NIEM National Information
ExchangeModel24nc:VehicleBrandnc:VehicleBrandCodenc:VehicleBrandDatenc:VehicleBrandDesignationnc:VehicleInspectionJurisdictionAuthoritync:VehicleInspectionJurisdictionAuthorityTextnc:VehicleInspectionSafetyPassIndicatornc:VehicleInspectionSmogCertificateCodenc:VehicleInspectionStationIdentificationnc:VehicleInspectionTestCategoryTextnc:VehicleMotorCarrierIdentificationnc:VehicleOdometerReadingMeasurenc:VehicleOdometerReadingUnitCode
25. ber-Model Labels
Region.water.distanceBetweenLatrinesAndWaterSource
Region.water.fecalOrOralTransmittedDiseases How are these labels
used? No way to standardize or horizontally integrate Trying to
pack too much into each label Contain elements from several
asserted ontologies Need to be Decomposed into elements Relating
elements from different asserted ontologies Common events and
objects in an Area of Operations25 26. A better solution, begins
with the Web(net-centricity) You build a site Others discover the
site and they link to it The more they link, the more well known
thepage becomes (Google ) Your data becomes discoverable26 27. 1.
Each group creates a controlled vocabulary ofthe terms commonly
used in its domain, andcreates an ontology out of these terms
usingOWL syntax4. Binds this ontology to its data and makes
thesedata available on the Web5. The ontologies are linked e.g.
through their useof some common terms6. These links create links
among all the datasets,thereby creating a web of data7. We can all
share the same tags they arecalled internet addressesThe roots of
Semantic Technology 28. Where we stand today increasing
availability of semantically enhanceddata and semantic software
increasing use of OWL (Web Ontology Language)in attempts to create
useful integration of on-linedata and information Linked Open Data
the New Big Thing28 29. as of September 2010 29 30. The problem:
the more SemanticTechnology is successful, they more it failsThe
original idea was to break down silos viacommon controlled
vocabularies for the taggingof dataThe very success of the approach
leads to thecreation of ever new controlled vocabularies semantic
silos as ever more ontologies arecreated in ad hoc waysEvery
organization and sub-organization nowwants to have its own
ontologyThe Semantic Web framework as currentlyconceived and
governed by the W3C yieldsminimal standardization30 31. Divided we
fail31 32. United we also fail32 33. 33The problem of joint /
coalition operationsFireSupportLogisticsAir
OperationsIntelligenceCivil-MilitaryOperationsTargetingManeuver&BlueForceTracking
34. Towards ontology coordination34 35. An alternative
solution:Semantic EnhancementA distributed incremental strategy of
coordinatedannotation data remain in their original state (is
treated at arms length) tagged using interoperable ontologies
created in tandem allows flexible response to new needs, adjustable
in realtime can be as complete as needed, lossless, long-lasting
becauseflexible and responsive big bang for buck measurable benefit
even from first smallinvestmentsThe strategy works only to the
degree that it rests onshared governance and training35 36.
compare: legends for mapscompare: legends for maps36 37. compare:
legends for mapscommon legends allow (cross-border) integration37
38. The Gene OntologyMouseEcotope
GlyProtDiabetInGeneGluChemsphingolipidtransporteractivity38 39. The
Gene OntologyMouseEcotope GlyProtDiabetInGeneGluChemHolliday
junctionhelicase complex39 40. The Gene OntologyMouseEcotope
GlyProtDiabetInGeneGluChemsphingolipidtransporteractivity40 41.
Common legends help human beings use and understand
complexrepresentations of reality help human beings create useful
complexrepresentations of reality help computers process
complexrepresentations of reality help glue data togetherBut common
legends serve these purposesonly if the legends are developed in
acoordinated, non-redundant fashion41 42. International System of
Units42 43. RELATIONTO TIMEGRANULARITYCONTINUANT
OCCURRENTINDEPENDENT DEPENDENTORGAN
ANDORGANISMOrganism(NCBITaxonomy)AnatomicalEntity(FMA,CARO)OrganFunction(FMP,
CPRO) PhenotypicQuality(PaTO)BiologicalProcess(GO)CELL
ANDCELLULARCOMPONENTCell(CL)CellularComponent(FMA,
GO)CellularFunction(GO)MOLECULEMolecule(ChEBI, SO,RnaO,
PrO)Molecular Function(GO)Molecular Process(GO)The Open Biomedical
Ontologies (OBO) Foundry43 44. CONTINUANT OCCURRENTINDEPENDENT
DEPENDENTORGAN
ANDORGANISMOrganism(NCBITaxonomy)AnatomicalEntity(FMA,CARO)OrganFunction(FMP,
CPRO) PhenotypicQuality(PaTO)Organism-LevelProcess(GO)CELL
ANDCELLULARCOMPONENTCell(CL)CellularComponent(FMA,
GO)CellularFunction(GO)Cellular Process(GO)MOLECULEMolecule(ChEBI,
SO,RNAO, PRO)Molecular Function(GO)MolecularProcess(GO)rationale of
OBO Foundry coverageGRANULARITYRELATION TOTIME44 45. RELATIONTO
TIMEGRANULARITYCONTINUANT OCCURRENTINDEPENDENT DEPENDENTCOMPLEX
OFORGANISMSFamily, Community,Deme, PopulationOrganFunction(FMP,
CPRO)PopulationPhenotypePopulationProcessORGAN
ANDORGANISMOrganism(NCBITaxonomy)AnatomicalEntity(FMA,CARO)
PhenotypicQuality(PaTO)BiologicalProcess(GO)CELL
ANDCELLULARCOMPONENTCell(CL)CellularComponent(FMA,
GO)CellularFunction(GO)MOLECULEMolecule(ChEBI, SO,RnaO,
PrO)Molecular Function(GO)Molecular Process(GO)Population-level
ontologies 45 46. RELATIONTO TIMEGRANULARITYCONTINUANT
OCCURRENTINDEPENDENT DEPENDENTORGAN
ANDORGANISMOrganism(NCBITaxonomy)AnatomicalEntity(FMA,CARO)OrganFunction(FMP,
CPRO)PhenotypicQuality(PaTO)BiologicalProcess(GO)CELL
ANDCELLULARCOMPONENTCell(CL)CellularComponent(FMA,
GO)CellularFunction(GO)MOLECULEMolecule(ChEBI, SO,RnaO,
PrO)Molecular Function(GO)Molecular Process(GO)Environment
Ontologyenvironments46 47. What can semantic technology dofor you?
software, hardware, business processes, target domainsof interest
change rapidly but meanings of common words change only slowly
semantic technology allows these meanings to beencoded separately
from data files and from applicationcode decoupling of semantics
from data andapplications ontologies (controlled, logically
structured, vocabularies),which are used to enhance legacy and
source content to make these contents retrievable even by those
notinvolved in their creation to support integration of data
deriving from heterogeneoussources47 48. Creation of new ontology
consortia,modeled on the OBO Foundry48NIF Standard Neuroscience
InformationFrameworkeagle-IOntologiesused by VIVO and CTSAconnect
for publications,patents, credentials, data andsample
collectionsIDO Consortium Infectious Disease OntologycROP Common
ReferenceOntologies for Plants 49. RELATIONTO
TIMEGRANULARITYCONTINUANT OCCURRENTINDEPENDENT DEPENDENTORGAN
ANDORGANISMOrganism(NCBITaxonomy)AnatomicalEntity(FMA,CARO)OrganFunction(FMP,
CPRO) PhenotypicQuality(PaTO)BiologicalProcess(GO)CELL
ANDCELLULARCOMPONENTCell(CL)CellularComponent(FMA,
GO)CellularFunction(GO)MOLECULEMolecule(ChEBI, SO,RnaO,
PrO)Molecular Function(GO)Molecular Process(GO)what is the analogue
of this in the military domain?49 50. 50 51. The SE solution:
Ontology (only) at the I2WD center Establish common ontology
content, which we andour collaborators (and our software) control
Keep this content consistent and non-redundant as itevolves. Seek
semantic sharing only in the SE environment. so what SE brings is
semantic interoperability plusconstrained syntax it brings a kind
of substitute for semanticinteroperability of source data models,
throughthe use by annotators of ontologies from thesingle evolving
SE suite51 52. Distributed Common Ground System
Army(DCGS-A)Semantic Enhancementof the Dataspaceon the CloudDr.
Tatiana MalyutaNew York City College of Technologyof the City
University of New York 53. Integrated Store of Intelligence Data
Lossless integration without heavy pre-processing Ability to:
Incorporate multiple integration models / approaches /points of
view of data and data-semantics Perform continuous semantic
enrichment of the integratedstore Scalability53 54. Solution
Components Cloud implementation Cloudbase (Accumulo) Data
Representation and Integration Framework Comprehensive unified
representation of data, datasemantics, and metadata This work
funded by US Army CERDECIntelligence and Information Warfare
Directorate(I2WD) Current pilot project to extend experimentally
toother services/agencies54 55. Dealing with Semantic
Heterogeneityber-Model =Physical Integration.A separate data
storehomogenizingsemantics in aparticular data-model works only
forspecial cases, entailsloss and distortion ofdata and
semantics,creates a new datasilo.Virtual integration.A projection
onto ahomogeneous data-model exposed tousers is moreflexible, but
may havethe problem of dataavailability (e.g.military,
intelligence).Also, a particularhomogeneous modelhas limited
usage,does not expose allcontent, and does notsupport enrichment55
56. Ontology vs. Data Model Each ontology provides a comprehensive
synoptic view of adomain as opposed to the flat and partial
representationprovided by a data modelComputerSkillSingle Ontology
Multiple Data
modelsPersonPersonPersonNameFirstNameLastNamePersonSkillPersonName
NetworkSkill ProgrammingSkillIs-a Bearer-ofSkillLast Name First
Name SkillPerson Name Computer
SkillProgrammingSkillNetworkSkillSkill56 57. Sources Source
database Db1, with tables Person and Skill, containingperson data
and data pertaining to skills of different kinds,respectively.
Source database Db2, with the table Person, containing dataabout IT
personnel and their skills: Source database Db3, with the table
ProgrSkill, containing dataabout programmers skills:PersonID
SkillID111 222SkillID Name Description222 Java ProgrammingID
SkillDescr333 SQLEmplID SkillName444 Java57 58. Benefits of the
approach We can see how much manual effort the analystneeds to
apply in performing search without SE and even then the information
he will gain willbe meager in comparison with what is madeavailable
through the Index with SE.For example, if an analyst is familiar
with the labelsused in Db1 and is thus in a position to enter Name=
Java, his query will still return only: person 111.Directly salient
Db4 information will thus be missed.58 59. Towards Globalization
and Sharing Using the SE approachto create a SharedSemantic
Resource forthe IntelligenceCommunity to enableinteroperability
acrosssystems Applying it directly to orprojecting its contentson a
particularintegration solution59 60. Building the Shared Semantic
Resource Methodology of distributed incrementaldevelopment Training
Governance Common Architecture of Ontologies to supportconsistency,
non-redundancy, modularity Upper Level Ontology (BFO) Mid-Level
Ontologies Low Level Ontologies60 61. Governance Common governance
coordinating editors, one from each ontology, responsiblefor
managing changes and ensuring use of common bestpractices small
high-level board to manage interoperability How much can we embed
governance into software? How much can we embed governance into
training? analogy with military doctrine Question: Can military
doctrine help to bring about theneeded ontology coordination61 62.
Governance principles1. All ontologies are expressed in a common
shared syntax (initially OWL2.0; perhaps later supplemented by
CLIF) (Syntax for annotationsneeds to be fixed later; potentially
RDF.)2. Each ontology possesses a unique identifier space
(namespace) andeach term has a unique ID ending with an
alphanumeric string of theform GO:00001234563. Each ontology has a
unique responsible authority (a human being)4. If ontologies import
segments from other ontologies then importedterms should preserve
the original term ID (URI).5. Versioning: The ontology uses
procedures for identifying distinctsuccessive versions (via
URIs).6. Each ontology must be created through a process of
downwardpopulation from existing higher-level ontologies to ensure
a commonarchitecture62 63. Governance principles7. Each ontology
extends from BFO 2.08. Each lower-level ontology is orthogonal to
the other ontologies atthe same level within the ontology
hierarchy9. The ontologies include textual (human readable) and
logicaldefinitions for all terms.10. The ontology uses relations
which are unambiguously definedfollowing the pattern of definitions
laid down in the RelationOntology that is incorporated into BFO
2.011. Each ontology is developed collaboratively, so that in areas
ofoverlap between neighboring ontologies authors will settle on
adivision of terms.12. Ontologies are divided between asserted and
inferred the formerare stable reference ontologies; the latter are
combinations ofontology fragments designed for specific local
needs.63 64. Orthogonality For each domain, ensure convergence upon
a singleontology recommended for use by those who wish tobecome
involved with the initiative Thereby: avoid the need for mappings
which are in tooexpensive, too fragile, too difficult to keep
up-to-date asmapped ontologies change Orthogonality means: everyone
knows where to look to find out how toannotate each kind of data
everyone knows where to look to find content forapplication
ontologies64 65. Ontology traffic rule for Definitions all
definitions should be of the genus-speciesformA =def. a B which
Cswhere B is the parent term of A in the ontologyhierarchy65 66.
Ontologies are built as orthogonalmodules which form an
incrementallyevolving network scientists are motivated to commit
todeveloping ontologies because they will need intheir own work
ontologies that fit into thisnetwork users are motivated by the
assurance that theontologies they turn to are maintained
byexperts66 67. More benefits of orthogonality helps those new to
ontology to find what theyneed to find models of good practice
ensures mutual consistency of ontologies(trivially) and thereby
ensures additivity of annotations67 68. More benefits of
orthogonality No need to reinvent the wheel for each newdomain Can
profit from storehouse of lessons learned Can more easily reuse
what is made by others Can more easily reuse training Can more
easily inspect and criticize results ofothers work Leads to
innovations (e.g. Mireot, Ontofox) instrategies for combining
ontologies68 69. Continuant
OccurrentIndependentContinuantDependentContinuantcell
componentbiological processmolecular functionBasic Formal
Ontology69 70. Anatomy Ontology(FMA*,
CARO)EnvironmentOntology(EnvO)InfectiousDiseaseOntology(IDO*)BiologicalProcessOntology
(GO*)CellOntology(CL)CellularComponentOntology(FMA*, GO*)
PhenotypicQualityOntology(PaTO)Subcellular Anatomy Ontology
(SAO)Sequence Ontology(SO*) MolecularFunction(GO*)Protein
Ontology(PRO*)Extension Strategy + Modular Organization 70top
levelmid-leveldomainlevelInformation ArtifactOntology(IAO)Ontology
forBiomedicalInvestigations(OBI)Spatial Ontology(BSPO)Basic Formal
Ontology (BFO) 71. continuantindependentcontinuantportion
ofmaterialobjectfiat
objectpartobjectaggregateobjectboundarysitedependentcontinuantgenericallydependentcontinuantinformationartifactspecificallydependentcontinuantqualityrealizableentityfunctionroledispositionspatialregion0D-region1D-region2D-region3D-regionBFO:continuant71
72. occurrentprocessualentityprocessfiat
processpartprocessaggregateprocessboundaryprocessualcontextspatiotemporalregionscatteredspatiotemporalregionconnectedspatiotemporalregionspatiotemporalinstantspatiotemporalintervaltemporalregionscatteredtemporalregionconnectedtemporalregiontemporalinstanttemporalintervalBFO:occurrent72
73. More than 100 Ontologyprojects using
BFOhttp://www.ifomis.org/bfo/users 74. Basic Formal
OntologyContinuant Occurrentprocess,
eventIndependentContinuantthingDependentContinuantquality.... .....
.......typesinstances 75. Blinding Flash of the ObviousContinuant
Occurrentprocess,
eventIndependentContinuantthingDependentContinuantquality.... .....
.......quality dependson bearer 76. Blinding Flash of the
ObviousContinuant Occurrentprocess,
eventIndependentContinuantthingDependentContinuantquality, ....
..... .......event dependson participant 77. Occurrents depend on
participantsinstances15 May bombing5 April insurgency
attackoccurrent typesbombingattackparticipant typesexplosive
deviceterrorist group 78. Roles pertain not to what a thing
enduringly is,but to the part it plays, e.g. in some
operationContinuantOccurrentprocess,
eventIndependentContinuantthingDependentContinuantrole.... .....
.......process is changein quality 79. General lessons for ontology
successincorporated into BFOCommon traffic lawsLessons learned and
disseminated ascommon guidelines all developers aredoing it the
same wayOntologies built by domain experts 80. Universality (low
hanging fruit)Start with simple assertions which youknow to be
universally truehand part_of bodycell death is_a deathpneumococcal
bacterium is_a bacterium(Computers need to be led by the hand) 81.
Need to manage ontologychange how to ensure that resources invested
inan ontology now do not lose their valuewhen the ontology changes
through explicit versioning, and agovernance structure for
changemanagement to ensure evolution intandem of ontologies within
the networkedontology structure) 82. Experience with BFO inbuilding
ontologies providesa community of skilled ontology developers
andusersassociated logical toolsdocumentation for different types
of usersa methodology for building conformantontologies by starting
with BFO and populatingdownwards 83. ConclusionOntologists have
established bestpractices for building ontologies for linking
ontologies for evaluating ontologies for applying ontologieswhich
have been thoroughly tested in useand which conform precisely to
the extensionstrategy from a single upper level 84. with thanks to
LCL Dr. Bill MandrickSenior OntologistData
Tacticshttp://militaryontology.orgA Strategy for Military
Ontology84 85. Agenda Introductory Remarks Previous Information
Revolution Ontology & Military Symbology Asserted Ontologies
Inferencing Realizing the strategy85 86. 86Orders of Reality1st
order. Reality as it is. In the actionin the upper image to the
right, realityis what is, not what we think ishappening2nd Order.
Participant Perceptions.What we believe is happening as wepeer
through the fog of war.3rd Order. Reality as we record it.
Inreports, databases, ontologies. The gaps between the orders of
reality introduce risk. These gaps are not the onlyform of risk but
reducing these gaps contributes to reducing risk.86 87. 87Examples
of Conflation of the 3 Orders 88. 88Warfighters Information Sharing
EnvironmentFireSupportLogisticsAir
OperationsIntelligenceCivil-MilitaryOperationsTargetingManeuver&BlueForceTracking
89. Merriam-Websters CollegiateDictionaryJoint Publication 1-02 DoD
Dictionaryof Military and Related TermsJoint Publication 3-0 Joint
OperationsJoint Publication 3-13 Joint Commandand ControlJoint
Publication 3-24CounterinsurgencyJoint Publication 3-57
Civil-MilitaryOperationsJP 3-10, Joint Security Operations
inTheaterJoint Publication 3-16 MultinationalOperationsJoint
Publication 5-0 Joint OperationsPlanningAuthoritative
Referenceshttp://www.dtic.mil/doctrine/Warfighter LexiconControlled
VocabularyStableHorizontally IntegratedCommon Operational Picture89
90. 90Ontology(ies) that enables interoperability among members of
an Operations Centerand other warfighters. 91. 91JP 3-0OperationsJP
2-0IntelligenceJP 6-0CommSupportJP 4-0LogisticsJP
3-16MultinationalOperationsJP 3-33JTFHeadquartersJP 1-02DoD
DictionaryCivil-Military OperationsArea of Operations XXX XArea of
Responsibility XXXXXC2 Systems XXX XDoctrinal
PublicationsConsistent Terminology (Data Elements, Names and
Definitions)Area of Interest X XXKey: word for word 92. Previous
Information Revolution92 93. Previous Information Revolution 1800
Cartographic Revolution Explosion of production, dissemination and
useof cartography Revolutionary and Napoleonic wars Several
individual armies in the extended terrain New spatial order of
warfare Urgent need for new methods of spatialmanagement**SOURCE:
PAPER EMPIRES: MILITARY CARTOGRAPHY AND THE MANAGEMENT OF SPACE93
94. Standardizing Geospatial InformationTriangulationMilitary Grid
Reference SystemLatitude Longitude 94 95. Interoperable
Semantics(example: Anatomy & Physiology) Standardized Labels
Anatomical Continuants Physiological Occurrents Teachable
Inferencing Horizontally Integrated Sharing of Observations
Accumulated Knowledge95 96. Standardized
Symbols96GroundResistorCapacitor 97. Ontology & Military
Symbology Elements of Military Ontology Represent Entities and
Events found in militarydomains Used to develop the Common
OperationalPicture Used to develop Situational Awareness Used to
develop Situational Understanding Used for Operational Design Used
to Task Organize Forces Used to Design/Create Information Networks
Enhance the Military Decision Making Process97 98. Military
SymbologySample of Military Standard 2525 Military Symbology98 99.
Map Overlays99 100. Task OrganizingOntological methods are used in
the process ofTask-OrganizingA Task-Organization is the Output
(Product) ofTask OrganizingA Task-Organization is a Plan or part of
a PlanA Plan is an Information Content EntityTask-Organizing The
act of designing an operatingforce, support staff, or logistic
package of specific sizeand composition to meet a unique task or
mission.Characteristics to examine when task-organizing theforce
include, but are not limited to: training,experience, equipage,
sustainability, operatingenvironment, enemy threat, and mobility.
(JP 3-05)100 101. Operational DesignSource: FM 3-0
OperationsMilitary Ontologies help planners and operators see
andunderstand the relations between Entities and Events in thearea
of operations.Military Ontologies are prerequisites of military
innovationssuch as Airborne Operations, Combined Fires and
JointOperations.Military Ontologies are prerequisites for the
creation of effectiveinformation systems.Operational Design The
conception and construction of theframework that underpins a
campaign or major operation planand its subsequent execution. See
also campaign; majoroperation. (JP 3-0)101 102. Asserted
(Reference) Ontologies Generic Content Aggressive Reuse Multiple
Different Types of Context Better Definitions Prerequisite for
InferencingTarget ListTargetNominationListCandidateTarget
ListHigh-PayoffTarget ListProtectedTarget
ListIntelligenceProductGeospatialIntelligenceProductTargetIntelligenceProductSignalsIntelligenceProductHumanIntelligenceProduct
102 103. 103 104. Grids &Coordinates104 105. Information
Artifacts105 106. Information Artifacts106 107. Geospatial Feature
Descriptions107 108. Geographic Features& Geospatial Regions108
109. Geospatial Regions*CombineGeographic/Geospatialcontent from
otherontology (see next slide)109 110. Artifacts110 111.
Facilities111 112. Facility by Role112 113.
continuantindependentcontinuantportion ofmaterialobjectfiat
objectpartobjectaggregateobjectboundarysitedependentcontinuantgenericallydependentcontinuantinformationartifactspecificallydependentcontinuantqualityrealizableentityfunctionroledispositionspatialregion0D-region1D-region2D-region3D-regionBFO:continuant113
114. occurrentprocessualentityprocessfiat
processpartprocessaggregateprocessboundaryprocessualcontextspatiotemporalregionscatteredspatiotemporalregionconnectedspatiotemporalregionspatiotemporalinstantspatiotemporalintervaltemporalregionscatteredtemporalregionconnectedtemporalregiontemporalinstanttemporalintervalBFO:occurrent114
115. Vehicles115 116. Weapons116 117. Human Ontologies117 118.
Organizations118 119. Processes119 120. Infantry Company is part_of
a Battalion (Continuant to Continuant)Civil Affairs Team
participates_in a Civil Reconnaissance(Continuant to
Occurrent)Military Engagement is part_of a Battle Event (Occurrent
toOccurrent)House is a Building (Universal to Universal)3rd
Platoon, Alpha Company participates_in Combat Operations(Instance
to Universal)3rd Platoon, Alpha Company is located_at Forward
Operating BaseWarhorse (Instance to Instance)Relations: How Data
becomes Information120 121. Standardized Relations121 122.
ber-Model Labels Region.water.distanceBetweenLatrinesAndWaterSource
Region.water.fecalOrOralTransmittedDiseases How are these labels
used? No way to standardize or horizontally integrate Trying to
pack too much into each label Contain elements from several
asserted ontologies Need to be Decomposed into elements Relating
elements from different asserted ontologies Common events and
objects in an Area of Operations122 123. Asserted
OntologiesRegion.water.distanceBetweenLatrinesAndWaterSourceTribalRegionArea
OfOperationsGeospatialRegionVillageWater
SourceLatrineWellPondCesspoolAct OfMeasurementAct OfAnalysisAct
OfObservationActMeasurement
ResultDepthMeasurementResultHeightMeasurement
ResultDistanceMeasurement
ResultGeographicCoordinatesLatitudeLongitudeCoordinatesMilitary
GridReference
SystemCoordinatesUniversalTransverseMercatorCoordinates123 124.
locatedinhasroleRegion.water.fecalOrOralTransmittedDiseasesWellVillageAssessmentBacterialPathogenRoleParticularBacterialPathogenRoleCollectionofBacteriumCholeraEpidemicCholeracause_ofcause_ofinstance_ofinstance_of
instance_ofReportDate
TimeGroup150029OCT2010stampsinstance_ofdescribesinstance_of 125.
Relating Asserted
OntologiesRegion.water.fecalOrOralTransmittedDiseasesVirusProtozoanMicroorganismBacteriumWater
SourceLatrineWellPondCesspoolPathogenRoleConsumableRoleMedicinalRoleRoleDiseaseHepatitis
AShigellosisCholeraEventContaminationEventEpidemic
EventDiseaseTransmissionEventhas_rolehas_locationpart_ofcause_of125
126. locatednearUnpacking:
Region.water.distanceBetweenLatrinesAndWaterSourceLatrineWellVT 334
569DistanceMeasurementResultVillageNameKhanabadVillageVillageis_ainstance_ofGeopoliticalEntitySpatialRegionGeographicCoordinatesSetdesignatesinstance_oflocatedininstance_ofhaslocation
designateshaslocationinstance_ofinstance_of16
metersinstance_ofmeasurement_of 127. 127 128. Conclusions
Situational Understanding Shared Lexicon Horizontal Integration of
Preferred Labels Need Training & Governance128 129. Barry
Smith&Bill MandrickRealizing the Strategy:A Practical
Introduction toOntology Building129 130. Agenda Standardized
Processes Scoping the Domain Creating Initial Lexicon Initial
Ontology Feedback and Iteration Publish and Share130 131. 131 132.
Guide to aRepeatable ProcessforOntology Creation (v 0.1)132 133.
Scope the Domain 1.1 Subject Matter Expert (SME) Interaction 1.2
Identify Authoritative References 1.3 Survey Authoritative
References 1.4 Define the Domain 1.5 Describe the Domain 1.6 Devise
Metrics133 134. Merriam-Websters CollegiateDictionaryJoint
Publication 1-02 DoD Dictionaryof Military and Related TermsJoint
Publication 3-0 Joint OperationsJoint Publication 3-13 Joint
Commandand ControlJoint Publication 3-24CounterinsurgencyJoint
Publication 3-57 Civil-MilitaryOperationsJP 3-10, Joint Security
Operations inTheaterJoint Publication 3-16
MultinationalOperationsJoint Publication 5-0 Joint
OperationsPlanningAuthoritative
Referenceshttp://www.dtic.mil/doctrine/ 134 135. 135 136.
DefinitionsAttack Geography:A description of the geography
surrounding the IEDincident, such as road segment, buildings,
foliage,etc. Understanding the geography indicates enemyuse of
landscape to channel tactical response, slowfriendly movement, and
prevent pursuit of enemyforces.IED Attack Geography:A Geospatial
Region where some IED Incident takesplace.IED Attack Geography
Description:A Description of the physical features of
someGeospatial Region where an IED Incident takesplace.Original
Definition Improved Definition(s)136 137. Method of Emplacement:A
description of where the device was delivered, used, oremployed.
(original definition)Original Definition Improved
Definition(s)Method of IED Emplacement:A systematic procedure used
in the positioning of anImprovised Explosive Device.Method of IED
Emplacement Description:A description of the systematic procedure
used in thepositioning of an Improvised Explosive Device.Example 2:
Method of Emplacement137 138. Example 3: Method of EmploymentMethod
of Employment:A description of where the device was delivered,
used, oremployed. (original definition)Original Definition Improved
Definition(s)Method of IED Employment:A systematic procedure used
in the delivery of anImprovised Explosive Device.Method of IED
Employment Description:A description of the systematic procedure
used in thedelivery of an Improvised Explosive Device.138 139.
Doctrinal Definitionsintelligence estimate The appraisal, expressed
inwriting or orally, of available intelligence relating to
aspecific situation or condition with a view to determiningthe
courses of action open to the enemy or adversaryand the order of
probability of their adoption. (JP 2-0)139 140. Intelligence
Ontology SuiteNo. Ontology Prefix Ontology Full Name List of Terms1
AO Agent Ontology2 ARTO Artifact Ontology3 BFO Basic Formal
Ontology4 EVO Event Ontology5 GEO Geospatial Feature Ontology6 IIAO
Intelligence Information Artifact Ontology7 LOCO Location Reference
Ontology8 TARGO Target OntologyHome Introduction PMESII-PT ASCOPE
References LinksWelcome to the I2WD Ontology Suite!I2WD Ontology
Suite: A web server aimed to facilitate ontology visualization,
query, and development for the IntelligenceCommunity. I2WD Ontology
Suite provides a user-friendly web interface for displaying the
details and hierarchy of a specificontology term.140 141. I2WD
Ontology Suite141 142. A simple example of how ontologiescan
help142 143. 143Logically Inconsistent Terms in CJCSI4410.01E,
Standardized Terminology forAircraft Inventory ManagementPeter
Morosoff e-MapsDecember 12, 2012Ontology-100 Aircraft Inventory
Terms.ppt 144. 144 145. 145 146. 146Diagram of Terms Extracted from
CJCSI 4410.01ENote that the diagram shows two major child or sub
categories: total activeinventory (TAI) and total inactive
inventory (TII). Note also that TII has eightsubcategories, of
which foreign military sales is represented as one among equals.
147. 147Foreign military sales aircraft. Aircraft and UA instorage,
bailment, used as government-furnished property,on loan or lease
outside the Defense establishment, orotherwise not available to the
Military Services; includesaircraft for the purpose of sale to
foreign governments.(Source: DOD 5105.36-M.)Bailment
aircraft.Aircraft and UAfurnished to andunder the physicalcustody
of anongovernmentalorganizationpursuant to therequirements of
agovernment contract.Lease aircraft.Military aircraftand UA
providedto agencies andorganizationsoutside thefederalgovernment on
atemporary basis.Loan aircraft.Military aircraftand UAprovided
toother federalgovernmentdepartmentsand agencieson
atemporarybasis.Storageaircraft.Aircraft and UAremoved fromthe
activeinventory andheld for parts,disposal, or in
apreservedcondition.Aircraftfor thepurpose ofsale
toforeigngovern-mentsTerms Extracted from CJCSI 4410.01EThe
category foreign military sales aircraft is defined, however, as
having six subcategories.Three of the six categories in the lower
boxes (i.e., bailment, lease, and loan aircraft) arealso
represented in the chart on the previous slide as being mutually
exclusive from foreignmilitary sales. Such categorization impedes
machine inference and creates a situation whichwill record some
bailment aircraft under foreign military sales
aircraft.Aircraftnototherwiseavailable 148. 148Thoughts Prompted by
Ontological Concepts1. Someone who understands logic needs to work
with theoversight office for CJCI 4410.01E to develop a
betterstructure of categories..2. How should we represent that a
UH-1 helicopter isowned by the Navy and being used for training so
thatwe facilitate machine inference about the helicopter?3. The
essential characteristics of UH-1 helicopter is amember of that
category is (a) a vehicle intended to flythat (b) is kept aloft by
rotating blades. Their accidentalroles include the Service that
owns them, their use intraining, and them membership in the primary
trainingaircraft inventory. 149. 149Bottom Line1. The people who
drafted and approved CJCSI4410.01E probably did the best they could
withthe categorization concepts and methodsavailable to them.
Clearly, however, theirproduct, while suitable for use by people,
doesnot support development of IT that maximizesthe potential of IT
to share data and informationand to inference.2. DoD needs to
exploit the concepts andmethods of ontology if its
informationtechnology (IT) is to maximize efficiency andoperational
effectiveness. 150. 150Do we need joint doctrine formilitary
informatics?1. To support joint operations2. To do justice to the
increasing role ofinformatics systems in militaryoperations3. To
ensure consistent procurement4. To promote utility of software to
thewarfighter 151. Examples of military innovations151Artillery
massing fires in WWINote that at the beginning of the 20th Century
the US Army hadthe technical means and capabilities to employe
indirect fieldartillery fires on the battlefield. It was, however,
not until 1939that the field manual on the employment of indirect
field artilleryfires was published. I have attached some quotes on
the Armysslow start of effective indirect field artillery
fires.Dowding in WWIIRadar stations were, in isolation, sitting
ducks for Luftwaffe.Through a C2 terrain model and common lexicon
he created anetwork to watch over all of them, and over the
airbases andequipment they helped to defendPatraeus in IRAQPetreaus
FM 3-24 "Counterinsurgency" Doctrine turned thingsaround in Iraq
152. Question (From P. Morosoff)152Should we wait before commiting
militaryinformatics into Doctrine?The massing artillery fires
example shows thatcreating a first-class military capability
fromtechnology often waits decades until thedoctrinal publications
are produced.Capability created in Ft Sill around 1906Capability
committed to Doctrine in 1939 153. The capability for massing
timely andaccurate artillery fires by dispersedbatteries upon
single targets required real-time communications of a sort that
could create a common operational picture that could take accountof
new developments in the field thereby transforming dispersed
batteries into a single systemof interoperable modules. this was
achieved through a new type of information support (better maps,
timekeeping) a new type of governance and training new artillery
doctrine153/24 154. The capability for massing timely andaccurate
intelligence fireswill similarly require real-time pooling of
information of asort that can create a common operational picture
able to be constantlyupdated in light of new developments in the
field thereby transforming dispersed data artifacts within theCloud
into a single system of interoperable modulesThis will require in
turn a new type of support (for semantic enhancement of data) a new
type of governance and training new intelligence doctrine to
include applied semantics154/24 155. Why is doctrine needed WIKI
DOTLMPF http://en.wikipedia.org/wiki/DOTMLPF155 156. DOTLMPF156The
Joint Capabilities Integration Development System providesa
solution space that considers solutions involving anycombination
ofDoctrineOrganizationTrainingMaterielLeadershipPersonnelFacilitiesDOTLMPF
also serves as a mnemonic for staff planners toconsider certain
issues prior to undertaking a new effort. 157. How is DOTMLPF
interpreted?157Doctrine: the way we fight, e.g., emphasizing
maneuver warfarecombined air-ground campaigns.Organization: how we
organize to fight; divisions, air wings, Marine-AirGround Task
Forces (MAGTFs), etc.Training: how we prepare to fight tactically;
basic training to advancedindividual training, various types of
unit training, joint exercises, etc.Materiel: all the stuff
necessary to equip our forces, that is, weapons,spares, etc. so
they can operate effectively.Leadership and education: how we
prepare our leaders to lead the fightfrom squad leader to 4-star
general/admiral; professional development.Personnel: availability
of qualified people for peacetime, wartime, andvarious contingency
operationsFacilities: real property; installations and industrial
facilities (e.g.government owned ammunition production facilities)
that support ourforces. 158. How can DOTMLPF be applied tomilitary
informatics?158Doctrine: how does software contribute to the way we
fight, e.g., incombined air-ground campaigns.Organization: how are
informatics personnel organized in relation tomilitary
units?Training: how are informatics personnel trained?Materiel: all
the stuff necessary to equip our forces, that is, weapons,spares,
etc. so they can operate effectively.Leadership and education: how
we prepare our leaders to lead the fightfrom squad leader to 4-star
general/admiral; professional development.Personnel: availability
of qualified people for peacetime, wartime, andvarious contingency
operationsFacilities: real property; installations and industrial
facilities (e.g.government owned ammunition production facilities)
that support ourforces. 159. Ideas towards Joint Doctrine
forMilitary Informatics159Joint Doctrine contains the controlled
vocabulary,lexicon, and nomenclature for Equipment (Vehicles,
Weapons, Target Roles, etc...) Events (Operations, Planning Events,
TargetingEvents, Intelligence Events e.g. IntelligencePreparation
of the Battlespace , etc. Military Units/OrganizationsWhat is
needed is the same type of standardization forInformation Artifacts
(Reports, Assessments, Estimates,Target Lists, Matrices, Templates,
Images, Maps, etc.) 160. Doctrine for informatics (from P.
Morosoff)160 Doctrine is one of DoDs primary tools for creating a
militarycapability from equipment including software,
computernetworks, and servers. To what extent were Navy
capabilities created from newequipment? The development of new
equipment does not guarantee a newcapability. From my Marine Corps
experience, I am familiar with the role ofdoctrine in creating
amphibious warfare, naval gunfire for groupforces, Navy close air
support to ground troops, and CWC (I believeCWC now stands for
composite warfare commander). In each case, a doctrinal publication
provided a conceptual andprocedural framework that included
characteristics of equipment indescriptions formulated generally so
that new models of particulartypes of equipment can be introduced
without requiring amodification of the doctrinal manual 161.
Doctrine creates an image of the waywe fight (from P.
Morosoff)161Doctrine facilitates warfighters creating or revising
their mental images ofhow to use the equipment to create a
capability: a good doctrinalpublication explains a general problem
(e.g., how to get a gun on a ship tohit a small target on the land)
and then explains how to solve it towarfighters who may feel the
problem is impossible.General Petreaus FM 3-24,
"Counterinsurgency," is a classic example. Inhis case, the writing
of FM 3-24 came about 3 years after the problemarose. Equipped with
that manual and encouragement from Petreaus thecommander in Iraq,
the forces in Iraq turned things around. 162. 162Coordinated
Warfighter Ontologies areprerequisite for:Situational
AwarenessSituational UnderstandingCommon Operational
PictureOperational DesignTask OrganizingSystems AnalysisMilitary
Decision Making Process 163. Conclusions & Recommendations
Ontology process is part of all Operations War-Fighters, doctrine
writers, and IT developersneed to collaboratively develop and then
work off ofshared models ontologies Essential to Sense-Making and
Understanding Essential to Decision Making Essential to proper
domain representation Currently no Repeatable Process (RP) across
DoD Should Adopt and Refine RP across DoD Benefits to Operations,
Doctrine, Training, and ITDevelopment 164. Coda: Practical
Introduction toOntology Building (Toy Example)Werner Ceusters164
165. New York StateCenter of Excellence inBioinformatics & Life
SciencesR T UA simple battlefield ontology (from W.
Ceusters)building personvehicletank
soldierPOWweaponmortarsubmachinegun carobjectcorpseSpatial
regionlocated-intransforms-intoOntology 166. New York StateCenter
of Excellence inBioinformatics & Life SciencesR T UOntology
used for annotating a situationbuilding personvehicletank
soldierPOWweaponmortarsubmachinegun carobjectcorpseSpatial
regionlocated-intransforms-intoOntologySituation 167. New York
StateCenter of Excellence inBioinformatics & Life SciencesR T
UReferent Tracking (RT) used for representing a situation#1 #2 #3
#4 #10OntologySituationalmodelSituation#5 #6
#8#7usesusesusesusesusesbuilding personvehicletank
soldierPOWweaponmortarsubmachinegun carobjectcorpseSpatial
regionlocated-intransforms-inbuilding personvehicletank
soldierPOWweaponmortarsubmachinegun carobjectcorpseSpatial
regionlocated-intransforms-in 168. New York StateCenter of
Excellence inBioinformatics & Life SciencesR T Uuse the same
weaponuse the sametype ofweaponReferent Tracking preserves
identity#2 #3 #4 #10OntologySituationalmodelSituation#6
#8#7usesusesusesusesbuilding personvehicletank
soldierPOWweaponmortarsubmachinegun carobjectcorpseSpatial
regionlocated-intransforms-inbuilding personvehicletank
soldierPOWweaponmortarsubmachinegun carobjectcorpseSpatial
regionlocated-intransforms-in 169. New York StateCenter of
Excellence inBioinformatics & Life SciencesR T
UfaithfulSpecific relations versus generic relations#1 #2 #3 #4
#10OntologySituationalmodelSituation#5 #6
#8#7usesusesusesusesusesbuilding personvehicletank
soldierPOWweaponmortarsubmachinegun carobjectcorpseSpatial
regionlocated-intransforms-inbuilding personvehicletank
soldierPOWweaponmortarsubmachinegun carobjectcorpseSpatial
regionlocated-intransforms-in 170. New York StateCenter of
Excellence inBioinformatics & Life SciencesR T USpecific
relations versus generic
relationsOntologySituationalmodelSituationNOT faithfulusesbuilding
personvehicletank soldierPOWweaponmortarsubmachinegun
carobjectcorpseSpatial regionlocated-intransforms-inbuilding
personvehicletank soldierPOWweaponmortarsubmachinegun
carobjectcorpseSpatial regionlocated-intransforms-in 171. New York
StateCenter of Excellence inBioinformatics & Life SciencesR T
URepresentation of times when relations
hold#3OntologySituationalmodelSituationsoldierprivate sergeant
sergeant-majorat t1at t2at t3 172. New York StateCenter of
Excellence inBioinformatics & Life SciencesR T U#1
#2OntologySituationalmodelSituation#5 #6usesat t1usesat t1building
personvehicletank soldierPOWweaponmortarsubmachinegun
carobjectcorpseSpatial regionlocated-intransforms-inbuilding
personvehicletank soldierPOWweaponmortarsubmachinegun
carobjectcorpseSpatial regionlocated-intransforms-in 173. New York
StateCenter of Excellence inBioinformatics & Life SciencesR T
U#1 #2OntologySituationalmodelSituation#5usesat t2after the death
of #1 at t2building personvehicletank
soldierPOWweaponmortarsubmachinegun carobjectcorpseSpatial
regionlocated-intransforms-inbuilding personvehicletank
soldierPOWweaponmortarsubmachinegun carobjectcorpseSpatial
regionlocated-intransforms-in 174. New York StateCenter of
Excellence inBioinformatics & Life SciencesR T URT deals with
conflicting representations bykeeping track of sources#1
#2SituationalmodelSituation#5 #6usesat t1usesat t1usesat t2at
t3Ontology corpseasserts at t2 175. New York StateCenter of
Excellence inBioinformatics & Life SciencesR T U#1
#2SituationalmodelSituation#5 #6usesat t1usesat t1usesat t2at
t3Ontology corpseasserts at t4RT deals with conflicting
representations bykeeping track of sources 176. New York
StateCenter of Excellence inBioinformatics & Life SciencesR T
UAdvantages of Referent Tracking Preserves identity Allows to
assert relationships amongst entities thatare not generically true
Appropriate representation of the time whenrelationships hold Deals
with conflicting representations by keepingtrack of sources Mimics
the structure of reality
