distributed common ground system army (dcgs-a)ncor.buffalo.edu/oi2/slides/big military data -...

Distributed Common Ground System – Army

(DCGS-A)

Barry Smith

Director

National Center for Ontological Research

The Role of Ontology in the Era

of Big (Military) Data

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Distributed Development of a Shared Semantic Resource (SSR)

in support of US Army’s Distributed Common

Ground System Standard Cloud (DSC) initiative

with thanks to: Tanya Malyuta, Ron Rudnicki

Background materials: http://x.co/yYxN

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http://x.co/yYxN

http://x.co/yYxN

Making data (re-)usable through common controlled vocabularies

• Allow multiple databases to be treated as if they were a single data source by eliminating terminological redundancy in ways data are described – not ‘Person’, and ‘Human’, and ‘Human Being’, and

‘Pn’, and ‘HB’, but simply: person

• Allow development and use of common tools and techniques, common training, single validation of data, focused around – semantic technology – coordinated ontology development and use

4

Ontology =def.

• controlled vocabulary organized as a graph • nodes in the graph are terms representing types

in reality • each node is associated with definition and

synonyms • edges in the graph represent well-defined

relations between these types • the graph is structured hierarchically via subtype

relations

5

Ontologies

• computer-tractable representations of types in specific areas of reality

• divided into more and less general

– upper = organizing ontologies, provide common architecture and thus promote interoperability

– lower = domain ontologies, provide grounding in reality

• reflecting top-down and bottom-up strategy

6

Success story in biomedicine Goal: integration of biological and clinical data

– across different species

– across levels of granularity (organ, organism, cell, molecule)

– across different perspectives (physical, biological, clinical)

– within and across domains (growth, aging, environment, genetic disease, toxicity …)

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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 9

RELATION TO TIME

GRANULARITY



COMPLEX OF ORGANISMS

Family, Community, Population

Organ Function

(FMP, CPRO)

Population Phenotype

Population Process

ORGAN AND ORGANISM

Organism (NCBI

Taxonomy)


Phenotypic Quality (PaTO)

Biological Process

(GO) CELL AND CELLULAR

COMPONENT

Cell (CL)


Cellular Function

(GO)

MOLECULE Molecule




Population-level ontologies 10

RELATION TO TIME

GRANULARITY



ORGAN AND ORGANISM

Organism (NCBI

Taxonomy)


Organ Function

(FMP, CPRO) Phenotypic

Quality (PaTO)

Biological Process

(GO)

CELL AND CELLULAR

COMPONENT

Cell (CL)


Cellular Function

(GO)

MOLECULE Molecule




Environment Ontology

En

vir

on

men

t

On

tolo

gy

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ORGAN AND ORGANISM

Organism (NCBI

Taxonomy)


Organ Function

(FMP, CPRO) Phenotypic Quality (PaTO)

Organism-Level Process

(GO)

CELL AND CELLULAR

COMPONENT

Cell (CL)


Cellular Function

(GO)

Cellular Process (GO)

MOLECULE Molecule

(ChEBI, SO, RNAO, PRO)


Molecular Process

(GO)

rationale of OBO Foundry coverage

GRANULARITY

RELATION TO

TIME

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OBO Foundry approach extended into other domains

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NIF Standard

Neuroscience Information Framework

ISF Ontologies Integrated Semantic Framework

OGMS and Extensions Ontology for General Medical Science

IDO Consortium Infectious Disease Ontology

cROP Common Reference Ontologies for Plants

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*)

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top level

domain level

Basic Formal Ontology (BFO)

Modular organization + Extension strategy

~100 ontologies using BFO

US Army Biometrics Ontology

Brucella Ontology (IDO-BRU)

eagle-i and VIVO (NCRR)

Financial Report Ontology (to support SEC through XBRL)

IDO Infectious Disease Ontology (NIAID)

Malaria Ontology (IDO-MAL)

Nanoparticle Ontology (NPO)

Ontology for Risks Against Patient Safety

(RAPS/REMINE)

Parasite Experiment Ontology (PEO)

Subcellular Anatomy Ontology (SAO)

Vaccine Ontology (VO)

… 15

Basic Formal Ontology

Thursday, April 18, 2013 16

BFO:Entity

BFO:Continuant BFO:Occurrent

BFO:Process BFO:Independent Continuant

BFO

BFO:Dependent Continuant

BFO:Disposition

Basic Formal Ontology and Mental Functioning Ontology (MFO)


BFO:Entity


BFO:Process

Organism

BFO:Independent Continuant

BFO

MFO


Behaviour inducing state

Mental Functioning Related Anatomical

Structure

Cognitive Representation

BFO:Quality

Affective Representation

Mental Process

Bodily Process BFO:Disposition

BFO:Entity


BFO:Process BFO:Independent

Continuant

BFO

MFO


Cognitive Representation

Affective Representation

Mental Process

Bodily Process BFO:Disposition

MFO-EM

Emotion Occurrent

Organism

Emotional Action Tendencies

Appraisal

Subjective Emotional Feeling

Physiological Response to

Emotion Process

inheres_in

is_output_of

Emotional Behavioural Process

Appraisal Process

has_part

agent_of

Emotion Ontology extends MFO

Sample from Emotion Ontology: Types of Feeling


The problem of joint / coalition operations

Fire

Support

Logistics Air

Operations

Intelligence

Civil-Military

Operations

Targeting

Maneuver

&

Blue

Force

Tracking

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US DoD Civil Affairs strategy for non-classified information sharing

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Ontologies / semantic technology

can help to solve this problem

Fire

Support

Logistics Air

Operations

Intelligence

Civil-Military

Operations

Targetin

g

Maneuver

&

Blue Force

Tracking

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But each community produces its own ontology,

this will merely create new, semantic siloes

Fire

Support

Logistics Air

Operations

Intelligence

Civil-Military

Operations

Targeting

Maneuver

&

Blue

Force

Tracking

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What we are doing to avoid the problem of semantic siloes

Distributed Development of a Shared Semantic Resource

Pilot testing to demonstrate feasibility

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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*)

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top level

domain level

Basic Formal Ontology (BFO)

creating the analog of this in the military domain

Semantic Enhancement

Annotation (tagging) of source data models using terms from coordinated ontologies

– data remain in their original state (are treated at arms length)

– tagged using interoperable ontologies created in tandem

– can be as complete as needed, lossless, long-lasting because flexible and responsive

– big bang for buck – measurable benefit even from first small investments

Coordination through shared governance and training

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Main challenge: Will it scale?

The problem of scalability turns on

• the ability to accommodate ever increasing volumes and types of data and numbers of users

• can we preserve coordination (consistency, non-redundancy) as ever more domains become involved?

• can we respond in agile fashion to ever changing bodies of source data?

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Strategy for agile ontology creation

• Identify or create carefully validated general purpose plug-and-play reference ontology modules for principal domains

• Develop a method whereby these reference ontologies can be extended very easily to cope with specific, local data through creation of application ontologies

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vehicle =def: an object used for transporting people or goods

tractor =def: a vehicle that is used for towing crane =def: a vehicle that is used for lifting and moving heavy objects

vehicle platform=def: means of providing mobility to a vehicle

wheeled platform=def: a vehicle platform that provides mobility through the use of wheels tracked platform=def: a vehicle platform that provides mobility through the use of continuous tracks

artillery vehicle = def. vehicle designed for the transport of one or more artillery weapons wheeled tractor = def. a tractor that has a wheeled platform

Russian wheeled tractor type T33 = def. a wheeled tractor of type T33 manufactured in Russia Ukrainian wheeled tractor type T33 = def. a wheeled tractor of type T33 manufactured in Ukraine

Reference Ontology Application Ontology

vehicle =def: an object used for transporting people or goods

tractor =def: a vehicle that is used for towing crane =def: a vehicle that is used for lifting and moving heavy objects

vehicle platform=def: means of providing mobility to a vehicle

wheeled platform=def: a vehicle platform that provides mobility through the use of wheels tracked platform=def: a vehicle platform that provides mobility through the use of continuous tracks

artillery vehicle = def. vehicle designed for the transport of one or more artillery weapons wheeled tractor = def. a tractor that has a wheeled platform

Russian wheeled tractor type T33 = def. a wheeled tractor of type T33 manufactured in Russia

Ukrainian wheeled tractor type T33 = def. a wheeled tractor of type T33 manufactured in Ukraine

Reference Ontology Application Ontology

Basic Formal Ontology

(BFO)

Extended Relation Ontology

Time Ontology

Quality Ontology

Information Entity

Ontology Geospatial Ontology

Event Ontology

Artifact Ontology

Agent Ontology

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http://milportal.org

An example of agile application ontology development:

The Bioweapons Ontology (BWO)

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Kinds of chemical and biological weapons

Chemical

Nerve agents (sarin gas)

Blister agents (mustard gas)

Blood agents (cyanide gas)

Biological

Infectious agents – BWO(I)

Toxic agents (botulinum toxin, ricin) – BWO(T)

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We focus here on BWO(I)

Infectious agents –Bacterial (anthrax, bubonic plague,

tularemia, brucellosis, cholera …)

–Viral (Ebola, Marburg …)

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BFO IDO StaphIDO

Independent

Continuant

Infectious

disorder

Staph. aureus

disorder

Dependent

Continuant

Infectious

disease

Protective

resistance

MRSA

Methicillin

resistance

Occurrent

Infectious

disease

course

MRSA course

Examples of ontology terms

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Infectious Disease Ontology (IDO)

IDO Core (Reference Ontology)

• General terms in the ID domain.

IDO Extensions (Application Ontologies)

• Disease-, host-, pathogen-specific.

• Developed by subject matter experts.

The hub-and-spokes strategy ensures that logical

content of IDO Core is automatically inherited by

the IDO Extensions

• with thanks to Lindsay Cowell (University of Texas SW Medical Center) and Albert Goldfain (Blue Highway, Inc.)

IDO Core

• Contains general terms in the ID domain:

– E.g., ‘colonization’, ‘pathogen’, ‘infection’

• A contract between IDO extension ontologies

and the datasets that use them.

• Intended to represent information along

several dimensions: – biological scale (gene, cell, organ, organism, population)

– discipline (clinical, immunological, microbiological)

– organisms involved (host, pathogen, and vector types)

BFO IDO StaphIDO

Independent

Continuant

Infectious

disorder

Staph. aureus

disorder

Dependent

Continuant

Infectious

disease

Protective

resistance

MRSA

Methicillin

resistance

Occurrent

Infectious

disease

course

MRSA course

Examples of ontology terms

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IDO Extensions

IDO – Brucellosis

IDO – Dengue Fever

IDO – Influenza

IDO – Malaria

IDO – Staphylococcus Aureus Bacteremia

IDO – Vector Surveillance and Management

IDO – Plant

VO – Vaccine Ontology

BWO(I) – Bioweapons Ontology (Infectious Agents)

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How IDO evolves: the case of Staph. aureus

IDOCore

IDOSa

IDOHumanSa

IDORatSa

IDOStrep

IDORatStrep

IDOHumanStrep

IDOMRSa

IDOHumanBacterial

IDOAntibioticResistant

IDOMAL IDOHIV HUB and SPOKES: Domain ontologies

SEMI-LATTICE: By subject matter experts in different communities of interest.

IDOFLU

BWO:disease by infectious agent

= def. a disease that is the consequence of the presence of pathogenic microbial agents, including pathogenic viruses, pathogenic bacteria, fungi, protozoa, multicellular parasites, and aberrant proteins known as prions

Strategy used to build BWO(I) with thanks to Lindsay Cowell and Oliver He (Michigan)

1. Start with a glossary such as: http://www.emedicinehealth.com/biological_warfare/

2. Select corresponding terms from IDO core and related ontologies such as the CHEBI Chemistry Ontology terms needed to describe bioweapons

3. All ontology terms keep their original definitions and IDs.

4. The result is a spreadsheet

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http://www.emedicinehealth.com/biological_warfare/

5. Where glossary terms have no ontology equivalent, create BWO ontology terms and

definitions as needed

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no corresponding ontology term

6. Use the Ontofox too to create the first version of the BWO(I) application ontology (http://ontofox.hegroup.org/)

7. Use BWO(I) in annotations, and where gaps are identified create extension terms, for instance – weaponized brucella

– aerosol anthrax

– smallpox incubation period

This establishes a virtuous cycle between ontology development and use in annotations

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http://ontofox.hegroup.org/

Potential uses of BWO

– semantic enhancement of bioweapons intelligence data

– results will be automatically interoperable with relevant bioinformatics and public health IT tools for dealing with infections, epidemics, vaccines, forensics, …

–to annotate research literature and research data on bioweapons

– to create computable definitions to substitute for definitions in free text glossaries

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Why do people think they need lexicons

• Training • Compiling lessons learned • Compiling results of testing, e.g. of proposed new

doctrine • Collective inferencing • Official reporting • Doctrinal development • Standard operating procedures • Sharing of data • People need to (ensure that they) understand

each other

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