Distributed Common Ground System Army (DCGS-A) The Role of Ontology in the Era of Big (Military) Data Barry Smith Director National Center for Ontological Research 1 Distributed Development of a Shared Semantic Resource (SSR) in support of US Armys Distributed Common Ground System Standard Cloud (DSC) initiative with thanks to: Tanya Malyuta, Ron Rudnicki Background materials: http://x.co/yYxN 2
3 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 ) 8 CONTINUANT OCCURRENT RELATION TO TIME INDEPENDENT
DEPENDENT GRANULARITY ORGAN AND ORGANISM CELL AND CELLULAR COMPONENT MOLECULE Organism (NCBI Taxonomy) Anatomica l Entity (FMA, CARO)
Organ Function (FMP, CPRO) Cell (CL) Cellular Compone nt (FMA, GO) Cellular Function (GO) Molecule (ChEBI, SO, RnaO, PrO)
Phenotypic Quality (PaTO) Molecular Function (GO) Biological Process (GO) Molecular Process (GO) The Open Biomedical Ontologies (OBO) Foundry 9 CONTINUANT
OCCURRENT RELATION TO TIME INDEPENDENT DEPENDENT GRANULARITY COMPLEX OF ORGANISMS Family, Community, Population ORGAN AND ORGANISM
Anatomica Organism l Entity (FMA, (NCBI Taxonomy) CARO) CELL AND CELLULAR COMPONENT MOLECULE Cell (CL) Cellular Componen t (FMA, GO) Molecule
(ChEBI, SO, RnaO, PrO) Organ Function (FMP, CPRO) Population Phenotype Phenotypic Quality (PaTO) Cellular Function (GO) Molecular Function (GO)
Population-level ontologies Population Process Biological Process (GO) Molecular Process (GO) 10 CONTINUANT OCCURRENT RELATION
TO TIME INDEPENDENT DEPENDENT ORGAN AND ORGANISM CELL AND CELLULAR COMPONENT MOLECULE Organism (NCBI Taxonomy) Anatomica l Entity (FMA,
CARO) Cell (CL) Cellular Compone nt (FMA, GO) Molecule (ChEBI, SO, RnaO, PrO) Environment Ontology GRANULARITY Organ Function
(FMP, CPRO) Cellular Function (GO) Phenotypic Quality (PaTO) Molecular Function (GO) Biological Process (GO) Molecular Process (GO)
Environment Ontology 11 RELATION TO TIME CONTINUANT INDEPENDENT OCCURRENT DEPENDENT GRANULARITY ORGAN AND ORGANISM CELL AND CELLULAR
COMPONENT MOLECULE Anatomic Organ OrganismOrganism al Entity Function (NCBI Level Process (FMA, (FMP, Taxonomy) (GO) Phenotypic CARO) CPRO) Quality Cellular (PaTO) Cellular
Cellular Cell Compone Function Process (CL) nt (GO) (GO) (FMA, GO) Molecule (ChEBI, SO, RNAO, PRO) Molecular Function (GO) Molecular Process (GO)
rationale of OBO Foundry coverage 12 OBO Foundry approach extended into other domains NIF Standard ISF Ontologies OGMS and Extensions IDO Consortium cROP Neuroscience Information Framework Integrated Semantic Framework Ontology for General Medical Science Infectious Disease Ontology
Common Reference Ontologies for Plants 13 Modular organization + Extension strategy top level Basic Formal Ontology (BFO) Anatomy Ontology (FMA*, CARO) Cellular Cell Component Ontology Ontology (CL) (FMA*, GO*) domain
level Environme nt Ontology (EnvO) Subcellular Anatomy Ontology (SAO) Sequence Ontology (SO*) Protein Ontology (PRO*) Infectious Disease Ontology (IDO*) Phenotypic
Quality Ontology (PaTO) Biological Process Ontology (GO*) Molecular Function (GO*) 14 ~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) national center for ontological research 15 NCOR Basic Formal Ontology BFO:Entity BFO
BFO:Disposition Physiological Response to Emotion Process Mental Process Cognitive Representation inheres_in Emotional Action Tendencies Bodily Process Appraisal Process
Affective Representation is_output_of Appraisal Emotional Behavioural Process Subjective Emotional Feeling has_part agent_of Emotion Occurrent Sample from Emotion Ontology: Types of Feeling
2/2/20 19 The problem of joint / coalition operations Intelligen ce Fire Support Targetin g Maneuv er & Blue Force Tracking
Air Operation CivilMilitary Operations Logistic s 23 US DoD Civil Affairs strategy for non-classified information sharing 24 Ontologies / semantic technology can help to solve this problem Intelligen ce
Fire Support Targeti ng Maneuve r& Blue Force Tracking Air Operation CivilMilitary Operations Logistic
s 25 But each community produces its own ontology, this will merely create new, semantic siloes Intelligen ce Fire Support Targeting Maneuv er & Blue Force Tracking
Air Operation CivilMilitary Operations Logistic s 26 What we are doing to avoid the problem of semantic siloes Distributed Development of a Shared Semantic Resource Pilot testing to demonstrate feasibility 27 creating the analog of this in the military domain
Process Ontology (GO*) Molecular Function (GO*) 28 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 29 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, nonredundancy) as ever more domains become involved? can we respond in agile fashion to ever changing bodies of source data? 31 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 32 Reference 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
Application Ontology 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 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 Application Ontology 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 AIRS Reference Ontologies Basic Formal Ontology (BFO) Extended Relation Ontology Agent Ontology Artifact Ontology
Event Ontology Geospatial Ontology Information Entity Ontology Quality Ontology Time Ontology Agent Ontology Social Network, Skills, and Occupations Event Ontology
Actions, Natural Events and Time-Dependent Attributes Geospatial Ontology Regions, Geopolitical Entities, Geographic Features, and Locations http://milportal.org 40 41 42 43 An example of agile application ontology development: The Bioweapons Ontology (BWO) 44
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) 45 We focus here on BWO(I) Infectious agents Bacterial (anthrax, bubonic plague, tularemia, brucellosis, cholera ) Viral (Ebola, Marburg ) 46
Infectious disease MRSA Protective resistance Methicillin resistance Infectious MRSA course disease course 47 Infectious Disease Ontology (IDO) with thanks to Lindsay Cowell (University of Texas SW
Medical Center) and Albert Goldfain (Blue Highway, Inc.) 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 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
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) 51 How IDO evolves: the case of Staph. aureus IDOMAL IDOFLU IDOCore IDORatSa
IDORatStrep HUB and SPOKES: Domain ontologies IDOStrep IDOSa IDOMRSa IDOHumanSa IDOHIV IDOAntibioticResistant SEMI-LATTICE: By subject matter experts in different
communities of IDOHumanStrep interest. IDOHumanBacterial 54 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 57 5. Where glossary terms have no ontology equivalent, create BWO ontology terms and definitions as needed no corresponding ontology term 58 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 59 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 60
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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