distributed common ground system army (dcgs-a)ncor.buffalo.edu/oi2/slides/big military data -...
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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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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
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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
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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
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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
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RELATION TO TIME
GRANULARITY
CONTINUANT OCCURRENT
INDEPENDENT DEPENDENT
COMPLEX OF ORGANISMS
Family, Community, 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 10
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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)
Environment Ontology
En
vir
on
men
t
On
tolo
gy
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CONTINUANT OCCURRENT
INDEPENDENT DEPENDENT
ORGAN AND ORGANISM
Organism (NCBI
Taxonomy)
Anatomical Entity (FMA, CARO)
Organ Function
(FMP, CPRO) Phenotypic 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
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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
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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)
Modular organization + Extension strategy
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~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)
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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
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Basic Formal Ontology and Mental Functioning Ontology (MFO)
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BFO:Entity
BFO:Continuant BFO:Occurrent
BFO:Process
Organism
BFO:Independent Continuant
BFO
MFO
BFO:Dependent Continuant
Behaviour inducing state
Mental Functioning Related Anatomical
Structure
Cognitive Representation
BFO:Quality
Affective Representation
Mental Process
Bodily Process BFO:Disposition
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BFO:Entity
BFO:Continuant BFO:Occurrent
BFO:Process BFO:Independent
Continuant
BFO
MFO
BFO:Dependent Continuant
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
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Sample from Emotion Ontology: Types of Feeling
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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
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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
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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
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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
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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.)
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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)
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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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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
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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
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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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5. Where glossary terms have no ontology equivalent, create BWO ontology terms and
definitions as needed
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no corresponding ontology term
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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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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