Towards Joint Doctrine for Military Informatics

Dr. Barry Smith
Director
National Center for Ontological Research
http://x.co/qtYq
Towards Joint Doctrine for Military
Informatics
1

Barry Smith – who am I?
Ontology work for
NextGen (Next Generation) Air Transportation System
National Nuclear Security Administration, DoE
Joint-Forces Command Joint Warfighting Center
Army Net-Centric Data Strategy Center of Excellence
Army Intelligence and Information Warfare Directorate
(I2WD)
and for many national and international biomedical
research and healthcare agencies
2

The problem of Big Data in biomedicine:
Multiple kinds of data in multiple kinds of silos
Lab / pathology data
Electronic Health Record data
Clinical trial data
Patient histories
Medical imaging
Microarray data
Protein chip data
Flow cytometry
Mass spec
Genotype / SNP data
each lab, each hospital, each agency has its own
terminology for describing this data 3

How to find your data?
How to reason with data when you find it?
How to understand the significance of the data
you collected 3 years earlier?
How to integrate with other people’s data?
Part of the solution must involve consensus-
based, standardized terminologies and coding
schemes
4

In the olden days
people measured lengths using inches, ulnas,
perches, king’s feet, Swiss feet, leagues of Paris,
etc., etc.
5

On June 22, 1799, in Paris,
everything changed
6

International System of Units
7

Making data (re-)usable through
standard terminologies
• Standards provide
– common structure and terminology
– single data source for review (less redundant
data)
• Standards allow
– use of common tools and techniques
– common training
– single validation of data
8

Unifying goal: integration of biological
and clinical data
– within and across domains
– across different species
– across levels of granularity (organ,
organism, cell, molecule)
– across different perspectives (physical,
biological, clinical)
9

One successful part of the solution to
this problem = Ontologies
controlled vocabularies (nomenclatures)
plus definitions of terms in a logical language
10

Ontologies
• are computer-tractable representations of
types in specific areas of reality
• are more and less general (upper and lower
ontologies)
– upper = organizing ontologies
– lower = domain ontologies
15

FMA
Pleural
Cavity
Interlobar
recess
Mesothelium
of Pleura
Pleura(Wall
of Sac)
Visceral
Pleura
Pleural Sac
Parietal
Pleura
Anatomical Space
Organ
Cavity
Serous Sac
Cavity
Anatomical
Structure
Organ
Serous Sac
Mediastinal
Pleura
Tissue
Organ Part
Organ
Subdivision
Organ
Component
Organ Cavity
Subdivision
Serous Sac
Cavity
Subdivision
Foundational Model of Anatomy
16

17
ontologies = standardized labels
designed for use in annotations
to make the data cognitively
accessible to human beings
and algorithmically accessible
to computers

by allowing grouping of annotations
brain 20
hindbrain 15
rhombomere 10
Query brain without ontology 20
Query brain with ontology 45
18
Ontologies facilitate retrieval of data

19
ontologies = high quality controlled
structured vocabularies used for the
annotation (description, tagging) of
data, images, emails, documents, …

The problem of retrieval, integration
and analysis of siloed data
• is not confined to biomedicine
• affects every domain due to massive legacy of
non-interoperable data models and data
systems
• and as new systems are created along the
same lines, the situation is constantly getting
worse.
20

The problem: many, many silos
• DoD spends more than $6B annually developing a
portfolio of more than 2,000 business systems
and Web services
• these systems are poorly integrated
• deliver redundant capabilities,
• make data hard to access, foster error and waste
• prevent secondary uses of data
https://ditpr.dod.mil/ Based on FY11 Defense Information Technology
Repository (DITPR) data
21

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

Favored solution: Über-model (NIEM,
JC3IEDM …)
– must be built en bloc beforehand
– inflexible, unresponsive to warfighter needs
– heavy-duty manual effort for both construction
and ingestion, with loss and/or distortion of
source 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 point
of view
23

NIEM National Information Exchange
Model
24
nc:VehicleBrand
nc:VehicleBrandCode
nc:VehicleBrandDate
nc:VehicleBrandDesignation
nc:VehicleInspectionJurisdictionAuthority
nc:VehicleInspectionJurisdictionAuthorityText
nc:VehicleInspectionSafetyPassIndicator
nc:VehicleInspectionSmogCertificateCode
nc:VehicleInspectionStationIdentification
nc:VehicleInspectionTestCategoryText
nc:VehicleMotorCarrierIdentification
nc:VehicleOdometerReadingMeasure
nc:VehicleOdometerReadingUnitCode

Über-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 Operations
25

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 the
page becomes (Google …)
• Your data becomes discoverable
26

1. Each group creates a controlled vocabulary of
the terms commonly used in its domain, and
creates an ontology out of these terms using
OWL syntax
4. Binds this ontology to its data and makes these
data available on the Web
5. The ontologies are linked e.g. through their use
of some common terms
6. These links create links among all the datasets,
thereby creating a „web of data‟
7. We can all share the same tags – they are
called internet addresses
The roots of Semantic Technology

Where we stand today
• increasing availability of semantically enhanced
data and semantic software
• increasing use of OWL (Web Ontology Language)
in attempts to create useful integration of on-line
data and information
• “Linked Open Data” the New Big Thing
28

The problem: the more Semantic
Technology is successful, they more it fails
The original idea was to break down silos via
common controlled vocabularies for the tagging
of data
The very success of the approach leads to the
creation of ever new controlled vocabularies –
semantic silos – as ever more ontologies are
created in ad hoc ways
Every organization and sub-organization now
wants to have its own “ontology”
The Semantic Web framework as currently
conceived and governed by the W3C yields
minimal standardization
30

33
The problem of joint / coalition operations
Fire
Support
LogisticsAir Operations
Intelligence
Civil-Military
Operations
Targeting
Maneuver
&
Blue
Force
Tracking

Towards ontology coordination
34

An alternative solution:
Semantic Enhancement
A distributed incremental strategy of coordinated
annotation
– 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 real
time
– can be as complete as needed, lossless, long-lasting because
flexible and responsive
– big bang for buck – measurable benefit even from first small
investments
The strategy works only to the degree that it rests on
shared governance and training
35

compare: legends for mapscompare: legends for maps
36

compare: legends for mapscommon legends allow (cross-border) integration
37

The Gene Ontology
MouseEcotope GlyProt
DiabetInGene
GluChem
sphingolipid
transporter
activity
38

The Gene Ontology
DiabetInGene
GluChem
Holliday junction
helicase complex
39

The Gene Ontology
DiabetInGene
GluChem
sphingolipid
transporter
activity
40

Common legends
• help human beings use and understand complex
representations of reality
• help human beings create useful complex
• help computers process complex
• help glue data together
But common legends serve these purposes
only if the legends are developed in a
coordinated, non-redundant fashion
41

International System of Units
42

RELATION
TO TIME
GRANULARITY
CONTINUANT OCCURRENT
INDEPENDENT DEPENDENT
ORGAN AND
ORGANISM
Organism
(NCBI
Taxonomy)
Anatomical
Entity
(FMA,
CARO)
Organ
Function
(FMP, CPRO) Phenotypic
Quality
(PaTO)
Biological
Process
(GO)
CELL AND
CELLULAR
COMPONENT
Cell
(CL)
Cellular
Component
(FMA, GO)
Cellular
Function
(GO)
MOLECULE
Molecule
(ChEBI, SO,
RnaO, PrO)
Molecular Function
(GO)
Molecular Process
(GO)
The Open Biomedical Ontologies (OBO) Foundry
43

ORGAN AND
ORGANISM
Organism
(NCBI
Taxonomy)
Anatomical
Entity
(FMA,
CARO)
Organ
Function
Quality
(PaTO)
Organism-Level
Process
(GO)
CELL AND
CELLULAR
COMPONENT
Cell
(CL)
Cellular
Component
(FMA, GO)
Cellular
Function
(GO)
Cellular Process
(GO)
MOLECULE
Molecule
(ChEBI, SO,
RNAO, PRO)
Molecular Function
(GO)
Molecular
Process
(GO)
rationale of OBO Foundry coverage
GRANULARITY
RELATION TO
TIME
44

RELATION
TO TIME
GRANULARITY
COMPLEX OF
ORGANISMS
Family, Community,
Deme, Population
Organ
Function
(FMP, CPRO)
Population
Phenotype
Population
Process
ORGAN AND
ORGANISM
Organism
(NCBI
Taxonomy)
Anatomical
Entity
(FMA,
CARO) Phenotypic
Quality
(PaTO)
Biological
Process
(GO)
CELL AND
CELLULAR
COMPONENT
Cell
(CL)
Cellular
Component
(FMA, GO)
Cellular
Function
(GO)
MOLECULE
Molecule
(ChEBI, SO,
RnaO, PrO)
Molecular Function
(GO)
Molecular Process
(GO)
Population-level ontologies 45

RELATION
TO TIME
GRANULARITY
ORGAN AND
ORGANISM
Organism
(NCBI
Taxonomy)
Anatomical
Entity
(FMA,
CARO)
Organ
Function
(FMP, CPRO)
Phenotypic
Quality
(PaTO)
Biological
Process
(GO)
CELL AND
CELLULAR
COMPONENT
Cell
(CL)
Cellular
Component
(FMA, GO)
Cellular
Function
(GO)
MOLECULE
Molecule
(ChEBI, SO,
RnaO, PrO)
Molecular Function
(GO)
Molecular Process
(GO)
Environment Ontology
environments
46

What can semantic technology do
for you?
• software, hardware, business processes, target domains
of interest change rapidly
• but meanings of common words change only slowly
• semantic technology allows these meanings to be
encoded separately from data files and from application
code – decoupling of semantics from data and
applications
• ontologies (controlled, logically structured, vocabularies),
which are used to enhance legacy and source content
− to make these contents retrievable even by those not
involved in their creation
− to support integration of data deriving from heterogeneous
sources
47

Creation of new ontology consortia,
modeled on the OBO Foundry
48
NIF Standard Neuroscience Information
Framework
eagle-I
Ontologies
used by VIVO and CTSA
connect for publications,
patents, credentials, data and
sample collections
IDO Consortium Infectious Disease Ontology
cROP Common Reference
Ontologies for Plants

RELATION
TO TIME
GRANULARITY
ORGAN AND
ORGANISM
Organism
(NCBI
Taxonomy)
Anatomical
Entity
(FMA,
CARO)
Organ
Function
Quality
(PaTO)
Biological
Process
(GO)
CELL AND
CELLULAR
COMPONENT
Cell
(CL)
Cellular
Component
(FMA, GO)
Cellular
Function
(GO)
MOLECULE
Molecule
(ChEBI, SO,
RnaO, PrO)
Molecular Function
(GO)
Molecular Process
(GO)
what is the analogue of this in the military domain?
49

The SE solution: Ontology (only) at the I2WD center
• Establish common ontology content, which we and
our collaborators (and our software) control
• Keep this content consistent and non-redundant as it
evolves.
• Seek semantic sharing only in the SE environment.
– so what SE brings is semantic interoperability plus
constrained syntax
– it brings a kind of substitute for semantic
interoperability of source data models, through
the use by annotators of ontologies from the
single evolving SE suite
51

Distributed Common Ground System – Army
(DCGS-A)
Semantic Enhancement
of the Dataspace
on the Cloud
Dr. Tatiana Malyuta
New York City College of Technology
of the City University of New York

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 integrated
store
• Scalability
53

Solution Components
• Cloud implementation
– Cloudbase (Accumulo)
• Data Representation and Integration Framework
– Comprehensive unified representation of data, data
semantics, and metadata
• This work funded by US Army CERDEC
Intelligence and Information Warfare Directorate
(I2WD)
• Current pilot project to extend experimentally to
other services/agencies
54

Dealing with Semantic Heterogeneity
Über-Model =
Physical Integration.
A separate data store
homogenizing
semantics in a
particular data-model
– works only for
special cases, entails
loss and distortion of
data and semantics,
creates a new data
silo.
Virtual integration.
A projection onto a
homogeneous data-
model exposed to
users – is more
flexible, but may have
the problem of data
availability (e.g.
military, intelligence).
Also, a particular
homogeneous model
has limited usage,
does not expose all
content, and does not
support enrichment
55

Ontology vs. Data Model
• Each ontology provides a comprehensive synoptic view of a
domain as opposed to the flat and partial representation
provided by a data model
Computer
Skill
Single Ontology Multiple Data models
PersonPerson
Person
Name
First
Name
Last
Name
PersonSkill
PersonName NetworkSkill ProgrammingSkill
Is-a Bearer-of
Skill
Last Name First Name Skill
Person Name Computer Skill
Programming
Skill
Network
Skill
Skill
56

Sources
• Source database Db1, with tables Person and Skill, containing
person data and data pertaining to skills of different kinds,
respectively.
• Source database Db2, with the table Person, containing data
about IT personnel and their skills:
• Source database Db3, with the table ProgrSkill, containing data
about programmers’ skills:
PersonID SkillID
111 222
SkillID Name Description
222 Java Programming
ID SkillDescr
333 SQL
EmplID SkillName
444 Java
57

Benefits of the approach
• We can see how much manual effort the analyst
needs to apply in performing search without SE
– and even then the information he will gain will
be meager in comparison with what is made
available through the Index with SE.
–For example, if an analyst is familiar with the labels
used 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

Towards Globalization and Sharing
• Using the SE approach
to create a Shared
Semantic Resource for
the Intelligence
Community to enable
interoperability across
systems
• Applying it directly to or
projecting its contents
on a particular
integration solution
59

Building the Shared Semantic Resource
• Methodology of distributed incremental
development
• Training
• Governance
• Common Architecture of Ontologies to support
consistency, non-redundancy, modularity
– Upper Level Ontology (BFO)
– Mid-Level Ontologies
– Low Level Ontologies
60

Governance
• Common governance
– coordinating editors, one from each ontology, responsible
for managing changes and ensuring use of common best
practices
– 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 the
needed ontology coordination
61

Governance principles
1. All ontologies are expressed in a common shared syntax (initially OWL
2.0; perhaps later supplemented by CLIF) (Syntax for annotations
needs to be fixed later; potentially RDF.)
2. Each ontology possesses a unique identifier space (namespace) and
each term has a unique ID ending with an alphanumeric string of the
form GO:0000123456
3. Each ontology has a unique responsible authority (a human being)
4. If ontologies import segments from other ontologies then imported
terms should preserve the original term ID (URI).
5. Versioning: The ontology uses procedures for identifying distinct
successive versions (via URIs).
6. Each ontology must be created through a process of downward
population from existing higher-level ontologies to ensure a common
architecture
62

Governance principles
7. Each ontology extends from BFO 2.0
8. Each lower-level ontology is orthogonal to the other ontologies at
the same level within the ontology hierarchy
9. The ontologies include textual (human readable) and logical
definitions for all terms.
10. The ontology uses relations which are unambiguously defined
following the pattern of definitions laid down in the Relation
Ontology that is incorporated into BFO 2.0
11. Each ontology is developed collaboratively, so that in areas of
overlap between neighboring ontologies authors will settle on a
division of terms.
12. Ontologies are divided between asserted and inferred – the former
are stable reference ontologies; the latter are combinations of
ontology fragments designed for specific local needs.
63

Orthogonality
• For each domain, ensure convergence upon a single
ontology recommended for use by those who wish to
become involved with the initiative
• Thereby: avoid the need for mappings – which are in too
expensive, too fragile, too difficult to keep up-to-date as
mapped ontologies change
• Orthogonality means:
– everyone knows where to look to find out how to
annotate each kind of data
– everyone knows where to look to find content for
application ontologies
64

Ontology traffic rule for Definitions
 all definitions should be of the genus-species
form
A =def. a B which Cs
where B is the parent term of A in the ontology
hierarchy
65

Ontologies are built as orthogonal
modules which form an incrementally
evolving network
• scientists are motivated to commit to
developing ontologies because they will need in
their own work ontologies that fit into this
network
• users are motivated by the assurance that the
ontologies they turn to are maintained by
experts
66

More benefits of orthogonality
• helps those new to ontology to find what they
need
• to find models of good practice
• ensures mutual consistency of ontologies
(trivially)
• and thereby ensures additivity of annotations
67

More benefits of orthogonality
• No need to reinvent the wheel for each new
domain
• 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 of
others’ work
• Leads to innovations (e.g. Mireot, Ontofox) in
strategies for combining ontologies
68

Continuant Occurrent
Independent
Continuant
Dependent
Continuant
cell component
biological process
molecular function
Basic Formal Ontology
69

Anatomy Ontology
(FMA*, CARO)
Environment
Ontology
(EnvO)
Infectious
Disease
Ontology
(IDO*)
Biological
Process
Ontology (GO*)
Cell
Ontology
(CL)
Cellular
Component
Ontology
(FMA*, GO*) Phenotypic
Quality
Ontology
(PaTO)
Subcellular Anatomy Ontology (SAO)
Sequence Ontology
(SO*) Molecular
Function
(GO*)Protein Ontology
(PRO*)
Extension Strategy + Modular Organization 70
top level
mid-level
domain
level
Information Artifact
Ontology
(IAO)
Ontology for
Biomedical
Investigations
(OBI)
Spatial Ontology
(BSPO)
Basic Formal Ontology (BFO)

continuant
independent
continuant
portion of
material
object
fiat object
part
object
aggregate
object
boundary
site
dependent
continuant
generically
dependent
continuant
information
artifact
specifically
dependent
continuant
quality
realizable
entity
function
role
disposition
spatial
region
0D-region
1D-region
2D-region
3D-region
BFO:continuant
71

occurrent
processual
entity
process
fiat process
part
process
aggregate
process
boundary
processual
context
spatiotemporal
region
scattered
spatiotemporal
region
connected
spatiotemporal
region
spatiotemporal
instant
spatiotemporal
interval
temporal
region
scattered
temporal
region
connected
temporal
region
temporal
instant
temporal
interval
BFO:occurrent
72

More than 100 Ontology
projects using BFO
http://www.ifomis.org/bfo/u
sers

Basic Formal Ontology
process, event
Independent
Continuant
thing
Dependent
Continuant
quality
.... ..... .......
types
instances

Blinding Flash of the Obvious
process, event
Independent
Continuant
thing
Dependent
Continuant
quality
.... ..... .......
quality depends
on bearer

Blinding Flash of the Obvious
process, event
Independent
Continuant
thing
Dependent
Continuant
quality, …
.... ..... .......
event depends
on participant

Occurrents depend on participants
instances
15 May bombing
5 April insurgency attack
occurrent types
bombing
attack
participant types
explosive device
terrorist group

Roles pertain not to what a thing enduringly is,
but to the part it plays, e.g. in some operation
Continuant
Occurrent
process, eventIndependent
Continuant
thing
Dependent
Continuant
role
.... ..... .......
process is change
in quality

General lessons for ontology success
incorporated into BFO
Common traffic laws
Lessons learned and disseminated as
common guidelines – all developers are
doing it the same way
Ontologies built by domain experts

Universality (low hanging fruit)
Start with simple assertions which you
know to be universally true
hand part_of body
cell death is_a death
pneumococcal bacterium is_a bacterium
(Computers need to be led by the hand)

Need to manage ontology
change
• how to ensure that resources invested in
an ontology now do not lose their value
when the ontology changes
• through explicit versioning, and a
governance structure for change
management to ensure evolution in
tandem of ontologies within the networked
ontology structure)

Experience with BFO in
building ontologies provides
a community of skilled ontology developers and
users
associated logical tools
documentation for different types of users
a methodology for building conformant
ontologies by starting with BFO and populating
downwards

Conclusion
Ontologists have established best
practices
– for building ontologies
– for linking ontologies
– for evaluating ontologies
– for applying ontologies
which have been thoroughly tested in use
and which conform precisely to the extension
strategy from a single upper level

with thanks to LCL Dr. Bill Mandrick
Senior Ontologist
Data Tactics
http://militaryontology.org
A Strategy for Military Ontology
84

Agenda
• Introductory Remarks
• Previous Information Revolution
• Ontology & Military Symbology
• Asserted Ontologies
• Inferencing
• Realizing the strategy
85

86
Orders of Reality
1st order. Reality as it is. In the action
in the upper image to the right, reality
is what is, not what we think is
happening
2nd Order. Participant Perceptions.
What we believe is happening as we
peer through the fog of war.
3rd Order. Reality as we record it. In
reports, databases, ontologies. …
The gaps between the orders of reality introduce risk. These gaps are not the only
form of risk but reducing these gaps contributes to reducing risk.
86

87
Examples of Conflation of the 3 Orders

88
Warfighters’ Information Sharing Environment
Fire
Support
LogisticsAir Operations
Intelligence
Civil-Military
Operations
Targeting
Maneuver
&
Blue
Force
Tracking

Merriam-Webster’s Collegiate
Dictionary
Joint Publication 1-02 DoD Dictionary
of Military and Related Terms
Joint Publication 3-0 Joint Operations
Joint Publication 3-13 Joint Command
and Control
Joint Publication 3-24
Counterinsurgency
Joint Publication 3-57 Civil-Military
Operations
JP 3-10, Joint Security Operations in
Theater
Joint Publication 3-16 Multinational
Operations
Planning
Authoritative References
http://www.dtic.mil/doctrine/
Warfighter Lexicon
Controlled Vocabulary
Stable
Horizontally Integrated
Common Operational Picture
89

90
Ontology(ies) that enables interoperability among members of an Operations Center
and other warfighters.

91
JP 3-0
Operations
JP 2-0
Intelligence
JP 6-0
Comm
Support
JP 4-0
Logistics
JP 3-16
Multinational
Operations
JP 3-33
JTF
Headquarters
JP 1-02
DoD Dictionary
Civil-Military Operations
Area of Operations XXX X
Area of Responsibility X
X
XX
X
C2 Systems X
X
X X
Doctrinal Publications
Consistent Terminology (Data Elements, Names and Definitions)
Area of Interest X X
X
Key: word for word

Previous Information Revolution
92

Previous Information Revolution
• 1800 Cartographic Revolution
• Explosion of production, dissemination and use
of cartography
• Revolutionary and Napoleonic wars
• Several individual armies in the extended terrain
• New spatial order of warfare
• Urgent need for new methods of spatial
management…*
*SOURCE: PAPER EMPIRES: MILITARY CARTOGRAPHY AND THE MANAGEMENT OF SPACE93

Standardizing Geospatial Information
Triangulation
Military Grid Reference System
Latitude Longitude 94

Interoperable Semantics
(example: Anatomy & Physiology)
• Standardized Labels
• Anatomical Continuants
• Physiological Occurrents
• Teachable
• Inferencing
• Horizontally Integrated
• Sharing of Observations
• Accumulated Knowledge
95

Standardized Symbols
96
Ground
Resistor
Capacitor

Ontology & Military Symbology
• Elements of Military Ontology
• Represent Entities and Events found in military
domains
• Used to develop the Common Operational
Picture
• 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 Process
97

Military Symbology
Sample of Military Standard 2525 Military Symbology
98

Task Organizing
Ontological methods are used in the process of
Task-Organizing
A Task-Organization is the Output (Product) of
Task Organizing
A Task-Organization is a Plan or part of a Plan
A Plan is an Information Content Entity
Task-Organizing — The act of designing an operating
force, support staff, or logistic package of specific size
and composition to meet a unique task or mission.
Characteristics to examine when task-organizing the
force include, but are not limited to: training,
experience, equipage, sustainability, operating
environment, enemy threat, and mobility. (JP 3-05)
100

Operational Design
Source: FM 3-0 Operations
Military Ontologies help planners and operators “see” and
understand the relations between Entities and Events in the
area of operations.
Military Ontologies are prerequisites of military innovations
such as Airborne Operations, Combined Fires and Joint
Operations.
Military Ontologies are prerequisites for the creation of effective
information systems.
Operational Design — The conception and construction of the
framework that underpins a campaign or major operation plan
and its subsequent execution. See also campaign; major
operation. (JP 3-0)
101

Asserted (Reference) Ontologies
• Generic Content
• Aggressive Reuse
• Multiple Different Types of Context
• Better Definitions
• Prerequisite for Inferencing
Target List
Target
Nomination
List
Candidate
Target List
High-Payoff
Target List
Protected
Target List
Intelligence
Product
Geospatial
Intelligence
Product
Target
Intelligence
Product
Signals
Intelligence
Product
Human
Intelligence
Product 102

Geospatial Feature Descriptions
107

Geographic Features
& Geospatial Regions
108

Geospatial Regions
*Combine
Geographic/Geospatial
content from other
ontology (see next slide)109

continuant
independent
continuant
portion of
material
object
fiat object
part
object
aggregate
object
boundary
site
dependent
continuant
generically
dependent
continuant
information
artifact
specifically
dependent
continuant
quality
realizable
entity
function
role
disposition
spatial
region
0D-region
1D-region
2D-region
3D-region
BFO:continuant
113

occurrent
processual
entity
process
fiat process
part
process
aggregate
process
boundary
processual
context
spatiotemporal
region
scattered
spatiotemporal
region
connected
spatiotemporal
region
spatiotemporal
instant
spatiotemporal
interval
temporal
region
scattered
temporal
region
connected
temporal
region
temporal
instant
temporal
interval
BFO:occurrent
114

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 to
Occurrent)
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 Base
Warhorse (Instance to Instance)
Relations: How Data becomes Information
120

Über-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 Operations
122

Asserted Ontologies
Region.water.distanceBetweenLatrinesAndWaterSource
Tribal
Region
Area Of
Operations
Geospatial
Region
Village
Water Source
Latrine
Well
Pond
CesspoolAct Of
Measurement
Act Of
Analysis
Act Of
Observation
Act
Measurement Result
Depth
Measurement
Result
Height
Measurement Result
Distance
Measurement Result
Geographic
Coordinates
Latitude
Longitude
Coordinates
Military Grid
Reference System
Coordinates
Universal
Transverse
Mercator
Coordinates
123

located
in
has
role
Region.water.fecalOrOralTransmittedDiseases
Well
Village
Assessment
Bacterial
Pathogen
Role
Particular
Bacterial
Pathogen
Role
Collection
of
Bacterium
Cholera
Epidemic
Cholera
cause_of
cause_of
instance_of
instance_of instance_of
Report
Date Time
Group
150029OCT2010
stamps
instance_of
describes
instance_of

Relating Asserted Ontologies
Region.water.fecalOrOralTransmittedDiseases
Virus
Protozoan
Microorganism
Bacterium
Water Source
Latrine
Well
Pond
Cesspool
Pathogen
Role
Consumable
Role
Medicinal
Role
Role
Disease
Hepatitis A
ShigellosisCholera
Event
Contamination
Event
Epidemic Event
Disease
Transmission
Event
has_role
has_location
part_of
cause_of
125

located
near
Unpacking: Region.water.distanceBetweenLatrinesAndWaterSource
Latrine
Well
‘VT 334 569’
Distance
Measurement
Result
Village
Name
‘Khanabad
Village’
Village
is_a
instance_of
Geopolitical
Entity
Spatial
Region
Geographic
Coordinates
Set
designates
instance_of
located
in
instance_of
has
location designates
has
location
instance_of
instance_of
’16 meters’
instance_of
measurement_of

Conclusions
• Situational Understanding
• Shared Lexicon
• Horizontal Integration of Preferred Labels
• Need Training & Governance
128

Barry Smith
&
Bill Mandrick
Realizing the Strategy:
A Practical Introduction to
Ontology Building
129

Agenda
• Standardized Processes
• Scoping the Domain
• Creating Initial Lexicon
• Initial Ontology
• Feedback and Iteration
• Publish and Share
130

Guide to a
Repeatable Process
for
Ontology Creation (v 0.1)
132

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 Metrics
133

Merriam-Webster’s Collegiate
Dictionary
Joint Publication 1-02 DoD Dictionary
of Military and Related Terms
Joint Publication 3-13 Joint Command
and Control
Joint Publication 3-24
Counterinsurgency
Joint Publication 3-57 Civil-Military
Operations
JP 3-10, Joint Security Operations in
Theater
Joint Publication 3-16 Multinational
Operations
Planning
Authoritative References
http://www.dtic.mil/doctrine/ 134

Definitions
Attack Geography:
A description of the geography surrounding the IED
incident, such as road segment, buildings, foliage,
etc. Understanding the geography indicates enemy
use of landscape to channel tactical response, slow
friendly movement, and prevent pursuit of enemy
forces.
IED Attack Geography:
A Geospatial Region where some IED Incident takes
place.
IED Attack Geography Description:
A Description of the physical features of some
Geospatial Region where an IED Incident takes
place.
Original “Definition” Improved Definition(s)
136

Method of Emplacement:
A description of where the device was delivered, used, or
employed. (original definition)
Method of IED Emplacement:
A systematic procedure used in the positioning of an
Improvised Explosive Device.
Method of IED Emplacement Description:
A description of the systematic procedure used in the
positioning of an Improvised Explosive Device.
Example 2: Method of Emplacement
137

Example 3: Method of Employment
Method of Employment:
A description of where the device was delivered, used, or
employed. (original definition)
Method of IED Employment:
A systematic procedure used in the delivery of an
Improvised Explosive Device.
Method of IED Employment Description:
A description of the systematic procedure used in the
delivery of an Improvised Explosive Device.
138

Doctrinal Definitions
intelligence estimate — The appraisal, expressed in
writing or orally, of available intelligence relating to a
specific situation or condition with a view to determining
the courses of action open to the enemy or adversary
and the order of probability of their adoption. (JP 2-0)
139

Intelligence Ontology Suite
No. Ontology Prefix Ontology Full Name List of Terms
1 AO Agent Ontology
2 ARTO Artifact Ontology
3 BFO Basic Formal Ontology
4 EVO Event Ontology
5 GEO Geospatial Feature Ontology
6 IIAO Intelligence Information Artifact Ontology
7 LOCO Location Reference Ontology
8 TARGO Target Ontology
Home Introduction PMESII-PT ASCOPE References Links
Welcome to the I2WD Ontology Suite!
I2WD Ontology Suite: A web server aimed to facilitate ontology visualization, query, and development for the Intelligence
Community. I2WD Ontology Suite provides a user-friendly web interface for displaying the details and hierarchy of a specific
ontology term.
140

A simple example of how ontologies
can help
142

143
Logically Inconsistent Terms in CJCSI
4410.01E, “Standardized Terminology for
Aircraft Inventory Management”
Peter Morosoff e-Maps
December 12, 2012
Ontology-100 Aircraft Inventory Terms.ppt

146
Diagram of Terms Extracted from CJCSI 4410.01E
Note that the diagram shows two major child or sub categories: total active
inventory (TAI) and total inactive inventory (TII). Note also that TII has eight
subcategories, of which foreign military sales is represented as one among equals.

147
Foreign military sales aircraft. Aircraft and UA in
storage, bailment, used as government-furnished property,
on loan or lease outside the Defense establishment, or
otherwise not available to the Military Services; includes
aircraft for the purpose of sale to foreign governments.
(Source: DOD 5105.36-M.)
Bailment aircraft.
Aircraft and UA
furnished to and
under the physical
custody of a
nongovernmental
organization
pursuant to the
requirements of a
government contract.
Lease aircraft.
Military aircraft
and UA provided
to agencies and
organizations
outside the
federal
government on a
temporary basis.
Loan aircraft.
Military aircraft
and UA
provided to
other federal
government
departments
and agencies
on a
temporary
basis.
Storage
aircraft.
Aircraft and UA
removed from
the active
inventory and
held for parts,
disposal, or in a
preserved
condition.
“Aircraft
for the
purpose of
sale to
foreign
govern-
ments”
Terms Extracted from CJCSI 4410.01E
The 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) are
also represented in the chart on the previous slide as being mutually exclusive from foreign
military sales. Such categorization impedes machine inference and creates a situation which
will record some bailment aircraft under “foreign military sales aircraft.”
“Aircraft
not
otherwise
available”

148
Thoughts Prompted by Ontological Concepts
1. Someone who understands logic needs to work with the
oversight office for CJCI 4410.01E to develop a better
structure of categories..
2. How should we represent that a UH-1 helicopter is
owned by the Navy and being used for training so that
we facilitate machine inference about the helicopter?
3. The essential characteristics of UH-1 helicopter is a
member of that category is (a) a vehicle intended to fly
that (b) is kept aloft by rotating blades. Their accidental
roles include the Service that owns them, their use in
training, and them membership in the “primary training
aircraft inventory.”

149
Bottom Line
1. The people who drafted and approved CJCSI
4410.01E probably did the best they could with
the categorization concepts and methods
available to them. Clearly, however, their
product, while suitable for use by people, does
not support development of IT that maximizes
the potential of IT to share data and information
and to inference.
2. DoD needs to exploit the concepts and
methods of ontology if its information
technology (IT) is to maximize efficiency and
operational effectiveness.

150
Do we need joint doctrine for
military informatics?
1. To support joint operations
2. To do justice to the increasing role of
informatics systems in military
operations
3. To ensure consistent procurement
4. To promote utility of software to the
warfighter

Examples of military innovations
151
Artillery massing fires in WWI
Note that at the beginning of the 20th Century the US Army had
the technical means and capabilities to employe indirect field
artillery fires on the battlefield. It was, however, not until 1939
that the field manual on the employment of indirect field artillery
fires was published. I have attached some quotes on the Army's
slow start of effective indirect field artillery fires.
Dowding in WWII
Radar stations were, in isolation, sitting ducks for Luftwaffe.
Through a C2 terrain model and common lexicon he created a
network to watch over all of them, and over the airbases and
equipment they helped to defend
Patraeus in IRAQ
Petreaus' FM 3-24 "Counterinsurgency" Doctrine turned things
around in Iraq

Question (From P. Morosoff)
152
Should we wait before commiting military
informatics into Doctrine?
The massing artillery fires example shows that
creating a first-class military capability from
technology often waits decades until the
doctrinal publications are produced.
Capability created in Ft Sill around 1906
Capability committed to Doctrine in 1939

The capability for massing timely and
accurate artillery fires by dispersed
batteries upon single targets required
• real-time communications of a sort that could
– create a common operational picture that could take account
of new developments in the field
– thereby transforming dispersed batteries into a single system
of interoperable modules.
• this was achieved through
– a new type of information support (better maps, timekeeping)
– a new type of governance and training
– new artillery doctrine
153/24

The capability for massing timely and
accurate intelligence “fires”
will similarly require real-time pooling of information of a
sort that can
– create a common operational picture able to be constantly
updated in light of new developments in the field
– thereby transforming dispersed data artifacts within the
Cloud into a single system of interoperable modules
This 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 semantics
154/24

Why is doctrine needed
• WIKI
• DOTLMPF
• http://en.wikipedia.org/wiki/DOTMLPF
155

DOTLMPF
156
The Joint Capabilities Integration Development System provides
a solution space that considers solutions involving any
combination of
Doctrine
Organization
Training
Materiel
Leadership
Personnel
Facilities
DOTLMPF also serves as a mnemonic for staff planners to
consider certain issues prior to undertaking a new effort.

How is DOTMLPF interpreted?
157
Doctrine: the way we fight, e.g., emphasizing maneuver warfare
combined air-ground campaigns.
Organization: how we organize to fight; divisions, air wings, Marine-Air
Ground Task Forces (MAGTFs), etc.
Training: how we prepare to fight tactically; basic training to advanced
individual 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 fight
from squad leader to 4-star general/admiral; professional development.
Personnel: availability of qualified people for peacetime, wartime, and
various contingency operations
Facilities: real property; installations and industrial facilities (e.g.
government owned ammunition production facilities) that support our
forces.

How can DOTMLPF be applied to
military informatics?
158
Doctrine: how does software contribute to the way we fight, e.g., in
combined air-ground campaigns.
Organization: how are informatics personnel organized in relation to
military 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 fight
from squad leader to 4-star general/admiral; professional development.
Personnel: availability of qualified people for peacetime, wartime, and
various contingency operations
Facilities: real property; installations and industrial facilities (e.g.
government owned ammunition production facilities) that support our
forces.

Ideas towards Joint Doctrine for
Military Informatics
159
Joint Doctrine contains the controlled vocabulary,
lexicon, and nomenclature for
• Equipment (Vehicles, Weapons, Target Roles, etc...)
• Events (Operations, Planning Events, Targeting
Events, Intelligence Events – e.g. Intelligence
Preparation of the Battlespace –, etc.
• Military Units/Organizations
What is needed is the same type of standardization for
Information Artifacts (Reports, Assessments, Estimates,
Target Lists, Matrices, Templates, Images, Maps, etc.)

Doctrine for informatics (from P. Morosoff)
160
• Doctrine is one of DoD's primary tools for creating a military
capability from equipment – including software, computer
networks, and servers.
• To what extent were Navy capabilities created from new
equipment?
• The development of new equipment does not guarantee a new
capability.
• From my Marine Corps experience, I am familiar with the role of
doctrine in creating amphibious warfare, naval gunfire for group
forces, Navy close air support to ground troops, and CWC (I believe
CWC now stands for composite warfare commander).
• In each case, a doctrinal publication provided a conceptual and
procedural framework that included characteristics of equipment in
descriptions formulated generally so that new models of particular
types of equipment can be introduced without requiring a
modification of the doctrinal manual

Doctrine creates an image of the way
we fight (from P. Morosoff)
161
Doctrine facilitates warfighters’ creating or revising their mental images of
how to use the equipment to create a capability: a good doctrinal
publication explains a general problem (e.g., how to get a gun on a ship to
hit a small target on the land) and then explains how to solve it to
warfighters who may feel the problem is impossible.
General Petreaus' FM 3-24, "Counterinsurgency," is a classic example. In
his case, the writing of FM 3-24 came about 3 years after the problem
arose. Equipped with that manual and encouragement from Petreaus the
commander in Iraq, the forces in Iraq turned things around.

162
Coordinated Warfighter Ontologies are
prerequisite for:
–Situational Awareness
–Situational Understanding
–Common Operational Picture
–Operational Design
–Task Organizing
–Systems Analysis
–Military Decision Making Process

Conclusions & Recommendations
• Ontology process is part of all Operations
• War-Fighters, doctrine writers, and IT developers
need to collaboratively develop and then work off of
shared 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 IT
Development

Coda: Practical Introduction to
Ontology Building (Toy Example)
Werner Ceusters
164

New York State
Center of Excellence in
Bioinformatics & Life Sciences
R T U
A simple battlefield ontology (from W. Ceusters)
building personvehicle
tank soldierPOW
weapon
mortar
submachine
gun car
object
corpse
Spatial region
located-in
transforms-into
Ontology

New York State
R T U
Ontology used for annotating a situation
tank soldierPOW
weapon
mortar
submachine
gun car
object
corpse
Spatial region
located-in
transforms-into
Ontology
Situation

New York State
R T U
Referent Tracking (RT) used for representing a situation
#1 #2 #3 #4 #10
Ontology
Situational
model
Situation
#5 #6 #8#7
usesuses
uses
uses
uses
tank soldierPOW
weapon
mortar
submachine
gun car
object
corpse
Spatial region
located-in
transforms-in
tank soldierPOW
weapon
mortar
submachine
gun car
object
corpse
Spatial region
located-in
transforms-in

New York State
R T U
use the same weapon
use the same
type of
weapon
Referent Tracking preserves identity
#2 #3 #4 #10
Ontology
Situational
model
Situation
#6 #8#7
uses
uses
uses
uses
tank soldierPOW
weapon
mortar
submachine
gun car
object
corpse
Spatial region
located-in
transforms-in
tank soldierPOW
weapon
mortar
submachine
gun car
object
corpse
Spatial region
located-in
transforms-in

New York State
R T U
faithful
Specific relations versus generic relations
#1 #2 #3 #4 #10
Ontology
Situational
model
Situation
#5 #6 #8#7
usesuses
uses
uses
uses
tank soldierPOW
weapon
mortar
submachine
gun car
object
corpse
Spatial region
located-in
transforms-in
tank soldierPOW
weapon
mortar
submachine
gun car
object
corpse
Spatial region
located-in
transforms-in

New York State
R T U
Specific relations versus generic relations
Ontology
Situational
model
Situation
NOT faithful
uses
tank soldierPOW
weapon
mortar
submachine
gun car
object
corpse
Spatial region
located-in
transforms-in
tank soldierPOW
weapon
mortar
submachine
gun car
object
corpse
Spatial region
located-in
transforms-in

New York State
R T U
Representation of times when relations hold
#3
Ontology
Situational
model
Situation
soldier
private sergeant sergeant-major
at t1
at t2
at t3

New York State
R T U
#1 #2
Ontology
Situational
model
Situation
#5 #6
uses
at t1
uses
at t1
tank soldierPOW
weapon
mortar
submachine
gun car
object
corpse
Spatial region
located-in
transforms-in
tank soldierPOW
weapon
mortar
submachine
gun car
object
corpse
Spatial region
located-in
transforms-in

New York State
R T U
#1 #2
Ontology
Situational
model
Situation
#5
uses
at t2
after the death of #1 at t2
tank soldierPOW
weapon
mortar
submachine
gun car
object
corpse
Spatial region
located-in
transforms-in
tank soldierPOW
weapon
mortar
submachine
gun car
object
corpse
Spatial region
located-in
transforms-in

New York State
R T U
RT deals with conflicting representations by
keeping track of sources
#1 #2
Situational
model
Situation
#5 #6
uses
at t1
uses
at t1
uses
at t2
at t3
Ontology corpse
asserts at t2

New York State
R T U
#1 #2
Situational
model
Situation
#5 #6
uses
at t1
uses
at t1
uses
at t2
at t3
Ontology corpse
asserts at t4
RT deals with conflicting representations by
keeping track of sources

New York State
R T U
Advantages of Referent Tracking
• Preserves identity
• Allows to assert relationships amongst entities that
are not generically true
• Appropriate representation of the time when
relationships hold
• Deals with conflicting representations by keeping
track of sources
• Mimics the structure of reality

Towards Joint Doctrine for Military Informatics

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