Distri Distribu bute ted d Commo Common n Gro Groun und System d System – Army 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 Army’s Distributed Common Ground System Standard Cloud (DSC) initiative with thanks to: Tanya Malyuta, Ron Rudnicki Background materials: http://x.co/yYxN 2
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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 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
RELATION CONTINUANT OCCURRENT TO TIME INDEPENDENT DEPENDENT GRANULARITY Anatomical Organism Organ ORGAN AND Entity (NCBI Function ORGANISM (FMA, Taxonomy) (FMP, CPRO) Phenotypic Biological CARO) Quality Process (PaTO) (GO) CELL AND Cellular Cellular Cell CELLULAR Component Function (CL) COMPONENT (FMA, GO) (GO) Molecule Molecular Function Molecular Process MOLECULE (ChEBI, SO, (GO) (GO) RnaO, PrO) The Open Biomedical Ontologies (OBO) Foundry 9
RELATION CONTINUANT OCCURRENT TO TIME INDEPENDENT DEPENDENT GRANULARITY COMPLEX OF Family, Community, Population Population ORGANISMS Population Phenotype Process Organ Anatomical Function ORGAN AND Organism Entity (FMP, CPRO) ORGANISM (NCBI (FMA, Phenotypic Taxonomy) CARO) Biological Quality Process (PaTO) (GO) CELL AND Cellular Cellular Cell CELLULAR Component Function (CL) COMPONENT (FMA, GO) (GO) Molecule Molecular Function Molecular Process MOLECULE (ChEBI, SO, (GO) (GO) RnaO, PrO) Population-level ontologies 10
RELATION CONTINUANT OCCURRENT TO TIME INDEPENDENT DEPENDENT GRANULARITY Anatomical Organism Organ ORGAN AND Entity (NCBI Function ORGANISM (FMA, Environment Taxonomy) (FMP, CPRO) Phenotypic Biological CARO) Ontology Quality Process (PaTO) (GO) CELL AND Cellular Cellular Cell CELLULAR Component Function (CL) COMPONENT (FMA, GO) (GO) Molecule Molecular Function Molecular Process MOLECULE (ChEBI, SO, (GO) (GO) RnaO, PrO) Environment Ontology 11
RELATION TO CONTINUANT OCCURRENT TIME INDEPENDENT DEPENDENT GRANULARITY Anatomical Organism Organ Organism-Level ORGAN AND Entity (NCBI Function Process ORGANISM (FMA, Taxonomy) (FMP, CPRO) Phenotypic (GO) CARO) Quality (PaTO) CELL AND Cellular Cellular Cell Cellular Process CELLULAR Component Function (CL) (GO) COMPONENT (FMA, GO) (GO) Molecule Molecular Molecular Function MOLECULE (ChEBI, SO, Process (GO) RNAO, PRO) (GO) rationale of OBO Foundry coverage 12
OBO Foundry approach extended into other domains 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 13
Modular organization + Extension strategy top level Basic Formal Ontology (BFO) Anatomy Ontology Infectious (FMA*, CARO) Disease Environment Ontology Ontology (IDO*) (EnvO) Cellular Cell Component Biological Ontology Ontology Process (CL) Phenotypic domain (FMA*, GO*) Ontology (GO*) Quality level Ontology Subcellular Anatomy Ontology (SAO) (PaTO) Sequence Ontology Molecular (SO*) Function Protein Ontology (GO*) (PRO*) 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) 15 …
Basic Formal Ontology BFO:Entity BFO BFO:Continuant BFO:Occurrent BFO:Independent BFO:Dependent BFO:Process Continuant Continuant BFO:Disposition Thursday, April 18, 2013 16
Basic Formal Ontology and Mental Functioning Ontology (MFO) BFO:Entity BFO BFO:Continuant BFO:Occurrent MFO BFO:Independent BFO:Dependent BFO:Process Continuant Continuant Bodily Process BFO:Disposition Organism Cognitive Representation BFO:Quality Mental Process Mental Functioning Related Anatomical Behaviour Structure inducing state Affective Representation Thursday, April 18, 2013 17
Emotion Ontology extends MFO BFO BFO:Entity MFO BFO:Continuant BFO:Occurrent MFO-EM BFO:Independent BFO:Dependent BFO:Process Continuant Continuant Organism Bodily Process BFO:Disposition Physiological Response to Emotion Process Mental Process Cognitive inheres_in Representation Appraisal Process Emotional Action Affective is_output_of Tendencies Appraisal Representation Emotional Behavioural Process Subjective Emotional Feeling has_part agent_of Emotion Occurrent
Sample from Emotion Ontology: Types of Feeling Thursday, April 18, 2013 19
The problem of joint / coalition operations Intelligence Fire Targeting Support Maneuver & Blue Force Tracking Civil-Military Air Logistics Operations 23 Operations
US DoD Civil Affairs strategy for non-classified information sharing 24
Ontologies / semantic technology can help to solve this problem Intelligence Fire Targetin Support g Maneuver & Blue Force Tracking Civil-Military Air Logistics Operations 25 Operations
But each community produces its own ontology, this will merely create new, semantic siloes Intelligence Fire Targeting Support Maneuver & Blue Force Tracking Civil-Military Air Logistics Operations Operations 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 top level Basic Formal Ontology (BFO) Anatomy Ontology Infectious (FMA*, CARO) Disease Environment Ontology Ontology (IDO*) (EnvO) Cellular Cell Component Biological Ontology Ontology Process (CL) Phenotypic domain (FMA*, GO*) Ontology (GO*) Quality level Ontology Subcellular Anatomy Ontology (SAO) (PaTO) Sequence Ontology Molecular (SO*) Function Protein Ontology (GO*) (PRO*) 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, non-redundancy) 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
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