The Role of Ontology in the Era of Big Military Data

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Ontology and information integration for military intelligence
http://ncor.buffalo.edu/OI2/

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  • Mental functioning related anatomical structure: an anatomical structure in which there inheres the disposition to be the agent of a mental processBehaviour inducing state: a bodily quality inhering in a mental functioning related anatomical structure which leads to behaviour of some sortAffective representation: a cognitive representation sustained by an organism about its own emotionsCognitive representation: a representation which specifically depends on an anatomical structure in the cognitive system of an organismMental process: a bodily process which brings into being, sustains or modifies a cognitive representation or a behaviour inducing state
  • Mental functioning related anatomical structure: an anatomical structure in which there inheres the disposition to be the agent of a mental processBehaviour inducing state: a bodily quality inhering in a mental functioning related anatomical structure which leads to behaviour of some sortAffective representation: a cognitive representation sustained by an organism about its own emotionsCognitive representation: a representation which specifically depends on an anatomical structure in the cognitive system of an organismMental process: a bodily process which brings into being, sustains or modifies a cognitive representation or a behaviour inducing state
  • The subjective feeling component of the emotion. These avoid sounding tautological/repetitive by focusing on distinct separable aspects of the feeling. It would be strange to include subjective feelings as separate components in the ontology if the best we could manage would be terms such as ‘feeling angry’, ‘feeling frightened’. Still, those sorts of feelings are often referred to in the scientific literature.
  • http://www.w3.org/People/Ivan/CorePresentations/HighLevelIntro/
  • http://www.w3.org/People/Ivan/CorePresentations/HighLevelIntro/
  • from Gerard Christman
  • http://www.enquirer.com/editions/1999/01/09/loc_types_of_chemical.html
  • http://www.slic2.wsu.edu:82/hurlbert/micro101/pages/101biologicalweapons.html#viralBW
  • Aims to overcome some of these obstacles.Ontologically correct natural language defs.
  • Subscribing to IDO core means agreeing to a semantics. Without bias to any dimension.
  • GrowthMicro ontologies for particular diseases that inherit terms and axioms about host-pathogen interaction. Evolve in a cross-producty way…so if you are interested in frog pneumonia, IDO Core has you covered.
  • The Role of Ontology in the Era of Big Military Data

    1. 1. Distributed Common Ground System – Army(DCGS-A)Barry SmithDirectorNational Center for Ontological ResearchThe Role of Ontology in the Eraof Big (Military) Data1
    2. 2. Distributed Development of aShared Semantic Resource (SSR)in support of US Army’s Distributed CommonGround System Standard Cloud (DSC) initiativewith thanks to: Tanya Malyuta, Ron RudnickiBackground materials: http://x.co/yYxN2
    3. 3. 3
    4. 4. Making data (re-)usable throughcommon controlled vocabularies• Allow multiple databases to be treated as ifthey were a single data source by eliminatingterminological redundancy in ways data aredescribed– not ‘Person’, and ‘Human’, and ‘Human Being’, and‘Pn’, and ‘HB’, but simply: person• Allow development and use of common toolsand techniques, common training, singlevalidation of data, focused around– semantic technology– coordinated ontology development and use4
    5. 5. Ontology =def.• controlled vocabulary organized as a graph• nodes in the graph are terms representing typesin reality• each node is associated with definition andsynonyms• edges in the graph represent well-definedrelations between these types• the graph is structured hierarchically via subtyperelations5
    6. 6. Ontologies• computer-tractable representations of typesin specific areas of reality• divided into more and less general– upper = organizing ontologies, provide commonarchitecture and thus promote interoperability– lower = domain ontologies, provide grounding inreality• reflecting top-down and bottom-up strategy6
    7. 7. Success story in biomedicineGoal: 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
    8. 8. RELATIONTO TIMEGRANULARITYCONTINUANT OCCURRENTINDEPENDENT DEPENDENTORGAN ANDORGANISMOrganism(NCBITaxonomy)AnatomicalEntity(FMA,CARO)OrganFunction(FMP, CPRO) PhenotypicQuality(PaTO)BiologicalProcess(GO)CELL ANDCELLULARCOMPONENTCell(CL)CellularComponent(FMA, GO)CellularFunction(GO)MOLECULEMolecule(ChEBI, SO,RnaO, PrO)Molecular Function(GO)Molecular Process(GO)The Open Biomedical Ontologies (OBO) Foundry9
    9. 9. RELATIONTO TIMEGRANULARITYCONTINUANT OCCURRENTINDEPENDENT DEPENDENTCOMPLEX OFORGANISMSFamily, Community,PopulationOrganFunction(FMP, CPRO)PopulationPhenotypePopulationProcessORGAN ANDORGANISMOrganism(NCBITaxonomy)AnatomicalEntity(FMA,CARO) PhenotypicQuality(PaTO)BiologicalProcess(GO)CELL ANDCELLULARCOMPONENTCell(CL)CellularComponent(FMA, GO)CellularFunction(GO)MOLECULEMolecule(ChEBI, SO,RnaO, PrO)Molecular Function(GO)Molecular Process(GO)Population-level ontologies 10
    10. 10. RELATIONTO TIMEGRANULARITYCONTINUANT OCCURRENTINDEPENDENT DEPENDENTORGAN ANDORGANISMOrganism(NCBITaxonomy)AnatomicalEntity(FMA,CARO)OrganFunction(FMP, CPRO)PhenotypicQuality(PaTO)BiologicalProcess(GO)CELL ANDCELLULARCOMPONENTCell(CL)CellularComponent(FMA, GO)CellularFunction(GO)MOLECULEMolecule(ChEBI, SO,RnaO, PrO)Molecular Function(GO)Molecular Process(GO)Environment OntologyEnvironmentOntology11
    11. 11. CONTINUANT OCCURRENTINDEPENDENT DEPENDENTORGAN ANDORGANISMOrganism(NCBITaxonomy)AnatomicalEntity(FMA,CARO)OrganFunction(FMP, CPRO) PhenotypicQuality(PaTO)Organism-LevelProcess(GO)CELL ANDCELLULARCOMPONENTCell(CL)CellularComponent(FMA, GO)CellularFunction(GO)Cellular Process(GO)MOLECULEMolecule(ChEBI, SO,RNAO, PRO)Molecular Function(GO)MolecularProcess(GO)rationale of OBO Foundry coverageGRANULARITYRELATION TOTIME12
    12. 12. OBO Foundry approach extended intoother domains13NIF Standard NeuroscienceInformation FrameworkISF Ontologies Integrated SemanticFrameworkOGMS and Extensions Ontology for GeneralMedical ScienceIDO Consortium Infectious DiseaseOntologycROP Common ReferenceOntologies for Plants
    13. 13. Anatomy Ontology(FMA*, CARO)EnvironmentOntology(EnvO)InfectiousDiseaseOntology(IDO*)BiologicalProcessOntology (GO*)CellOntology(CL)CellularComponentOntology(FMA*, GO*) PhenotypicQualityOntology(PaTO)Subcellular Anatomy Ontology (SAO)Sequence Ontology(SO*) MolecularFunction(GO*)Protein Ontology(PRO*)14top leveldomainlevelBasic Formal Ontology (BFO)Modular organization + Extension strategy
    14. 14. ~100 ontologies using BFOUS Army Biometrics OntologyBrucella 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
    15. 15. Basic Formal OntologyThursday, May 02, 2013 16BFO:EntityBFO:Continuant BFO:OccurrentBFO:ProcessBFO:IndependentContinuantBFOBFO:DependentContinuantBFO:Disposition
    16. 16. Basic Formal Ontologyand Mental Functioning Ontology (MFO)Thursday, May 02, 2013 17BFO:EntityBFO:Continuant BFO:OccurrentBFO:ProcessOrganismBFO:IndependentContinuantBFOMFOBFO:DependentContinuantBehaviourinducing stateMental FunctioningRelated AnatomicalStructureCognitiveRepresentationBFO:QualityAffectiveRepresentationMental ProcessBodily ProcessBFO:Disposition
    17. 17. BFO:EntityBFO:Continuant BFO:OccurrentBFO:ProcessBFO:IndependentContinuantBFOMFOBFO:DependentContinuantCognitiveRepresentationAffectiveRepresentationMental ProcessBodily ProcessBFO:DispositionMFO-EMEmotion OccurrentOrganismEmotional ActionTendenciesAppraisalSubjectiveEmotional FeelingPhysiologicalResponse toEmotion Processinheres_inis_output_ofEmotionalBehavioural ProcessAppraisalProcesshas_partagent_ofEmotion Ontology extends MFO
    18. 18. Sample from Emotion Ontology: Types of FeelingThursday, May 02, 2013 19
    19. 19. The problem of joint / coalition operationsFireSupportLogisticsAirOperationsIntelligenceCivil-MilitaryOperationsTargetingManeuver&BlueForceTracking23
    20. 20. US DoD Civil Affairs strategy for non-classifiedinformation sharing24
    21. 21. Ontologies / semantic technologycan help to solve this problemFireSupportLogisticsAirOperationsIntelligenceCivil-MilitaryOperationsTargetingManeuver&Blue ForceTracking25
    22. 22. But each community produces its own ontology,this will merely create new, semantic siloesFireSupportLogisticsAirOperationsIntelligenceCivil-MilitaryOperationsTargetingManeuver&BlueForceTracking26
    23. 23. What we are doing to avoid theproblem of semantic siloesDistributed Development of a SharedSemantic ResourcePilot testing to demonstrate feasibility27
    24. 24. Anatomy Ontology(FMA*, CARO)EnvironmentOntology(EnvO)InfectiousDiseaseOntology(IDO*)BiologicalProcessOntology (GO*)CellOntology(CL)CellularComponentOntology(FMA*, GO*) PhenotypicQualityOntology(PaTO)Subcellular Anatomy Ontology (SAO)Sequence Ontology(SO*) MolecularFunction(GO*)Protein Ontology(PRO*)28top leveldomainlevelBasic Formal Ontology (BFO)creating the analog of this in the military domain
    25. 25. Semantic EnhancementAnnotation (tagging) of source data models usingterms from coordinated ontologies– data remain in their original state (are treated at armslength)– tagged using interoperable ontologies created in tandem– can be as complete as needed, lossless, long-lastingbecause flexible and responsive– big bang for buck – measurable benefit even from firstsmall investmentsCoordination through shared governance andtraining29
    26. 26. Main challenge: Will it scale?The problem of scalability turns on• the ability to accommodate ever increasingvolumes and types of data and numbers ofusers• can we preserve coordination (consistency,non-redundancy) as ever more domainsbecome involved?• can we respond in agile fashion to everchanging bodies of source data?31
    27. 27. Strategy for agile ontology creation• Identify or create carefully validated generalpurpose plug-and-play reference ontologymodules for principal domains• Develop a method whereby these referenceontologies can be extended very easily to copewith specific, local data through creation ofapplication ontologies32
    28. 28. vehicle =def: an object used fortransporting people or goodstractor =def: a vehicle that is used fortowingcrane =def: a vehicle that is used forlifting and moving heavy objectsvehicle platform=def: means of providingmobility to a vehiclewheeled platform=def: a vehicleplatform that provides mobility throughthe use of wheelstracked platform=def: a vehicleplatform that provides mobility throughthe use of continuous tracksartillery vehicle = def. vehicle designed forthe transport of one or more artilleryweaponswheeled tractor = def. a tractor that has awheeled platformRussian wheeled tractor type T33 =def. a wheeled tractor of type T33manufactured in RussiaUkrainian wheeled tractor type T33= def. a wheeled tractor of type T33manufactured in UkraineReference Ontology Application Ontology
    29. 29. vehicle =def: an object used fortransporting people or goodstractor =def: a vehicle that isused for towingcrane =def: a vehicle that is used forlifting and moving heavy objectsvehicle platform=def: means of providingmobility to a vehiclewheeled platform=def: a vehicleplatform that provides mobility throughthe use of wheelstracked platform=def: a vehicleplatform that provides mobility throughthe use of continuous tracksartillery vehicle = def. vehicle designed forthe transport of one or more artilleryweaponswheeled tractor = def. a tractor that has awheeled platformRussian wheeled tractor type T33 =def. a wheeled tractor of type T33manufactured in RussiaUkrainian wheeled tractortype T33 = def. a wheeledtractor of type T33manufactured in UkraineReference Ontology Application Ontology
    30. 30. Basic FormalOntology(BFO)ExtendedRelationOntologyTimeOntologyQualityOntologyInformationEntityOntologyGeospatialOntologyEventOntologyArtifactOntologyAgentOntology
    31. 31. 40http://milportal.org
    32. 32. 41
    33. 33. 42
    34. 34. 43
    35. 35. An example of agile applicationontology development:The Bioweapons Ontology (BWO)44
    36. 36. Kinds of chemical and biologicalweaponsChemicalNerve agents (sarin gas)Blister agents (mustard gas)Blood agents (cyanide gas)BiologicalInfectious agents – BWO(I)Toxic agents (botulinum toxin, ricin) – BWO(T)45
    37. 37. We focus here on BWO(I)Infectious agents–Bacterial (anthrax, bubonic plague,tularemia, brucellosis, cholera …)–Viral (Ebola, Marburg …)46
    38. 38. BFO IDO StaphIDOIndependentContinuantInfectiousdisorderStaph. aureusdisorderDependentContinuantInfectiousdiseaseProtectiveresistanceMRSAMethicillinresistanceOccurrentInfectiousdiseasecourseMRSA courseExamples of ontology terms47
    39. 39. 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 logicalcontent of IDO Core is automatically inherited bythe IDO Extensions•with thanks to Lindsay Cowell (University of Texas SWMedical Center) and Albert Goldfain (Blue Highway, Inc.)
    40. 40. IDO Core• Contains general terms in the ID domain:– E.g., ‘colonization’, ‘pathogen’, ‘infection’• A contract between IDO extension ontologiesand the datasets that use them.• Intended to represent information alongseveral dimensions:– biological scale (gene, cell, organ, organism, population)– discipline (clinical, immunological, microbiological)– organisms involved (host, pathogen, and vector types)
    41. 41. BFO IDO StaphIDOIndependentContinuantInfectiousdisorderStaph. aureusdisorderDependentContinuantInfectiousdiseaseProtectiveresistanceMRSAMethicillinresistanceOccurrentInfectiousdiseasecourseMRSA courseExamples of ontology terms50
    42. 42. IDO ExtensionsIDO – BrucellosisIDO – Dengue FeverIDO – InfluenzaIDO – MalariaIDO – Staphylococcus Aureus BacteremiaIDO – Vector Surveillance and ManagementIDO – PlantVO – Vaccine OntologyBWO(I) – Bioweapons Ontology (Infectious Agents)51
    43. 43. How IDO evolves: the case of Staph.aureusIDOCoreIDOSaIDOHumanSaIDORatSaIDOStrepIDORatStrepIDOHumanStrepIDOMRSaIDOHumanBacterialIDOAntibioticResistantIDOMAL IDOHIVHUB andSPOKES:DomainontologiesSEMI-LATTICE:By subject matterexperts in differentcommunities ofinterest.IDOFLU
    44. 44. 54
    45. 45. BWO:disease by infectious agent= def. a disease that is the consequence of the presence ofpathogenic microbial agents, including pathogenic viruses,pathogenic bacteria, fungi, protozoa, multicellular parasites,and aberrant proteins known as prions
    46. 46. 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 andrelated ontologies such as the CHEBI ChemistryOntology terms needed to describe bioweapons3. All ontology terms keep their original definitionsand IDs.4. The result is a spreadsheet57
    47. 47. 5. Where glossary terms have no ontologyequivalent, create BWO ontology terms anddefinitions as needed58no correspondingontology term
    48. 48. 6. Use the Ontofox too to create the first version ofthe BWO(I) application ontology(http://ontofox.hegroup.org/)7. Use BWO(I) in annotations, and where gaps areidentified create extension terms, for instance– weaponized brucella– aerosol anthrax– smallpox incubation periodThis establishes a virtuous cycle between ontologydevelopment and use in annotations59
    49. 49. Potential uses of BWO– semantic enhancement of bioweaponsintelligence data– results will be automatically interoperable withrelevant bioinformatics and public health IT toolsfor dealing with infections, epidemics, vaccines,forensics, …–to annotate research literature and research dataon bioweapons– to create computable definitions to substitute fordefinitions in free text glossaries60
    50. 50. Why do people think they need lexicons• Training• Compiling lessons learned• Compiling results of testing, e.g. of proposed newdoctrine• Collective inferencing• Official reporting• Doctrinal development• Standard operating procedures• Sharing of data• People need to (ensure that they) understandeach other

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