Horizontal Integration of Warfighter Intelligence Data A Shared Semantic Resource for the Intelligence Community Barry Smith, University at Buffalo, NY, USA Tatiana Malyuta, New York City College of Technology, NY William S. Mandrick, Data Tactics Corp., VA, USA Chia Fu, Data Tactics Corp., VA, USA Kesny Parent, Intelligence and Information Warfare Directorate, CERDEC, MD, USA Milan Patel, Intelligence and Information Warfare Directorate, CERDEC, MD, USA

Horizontal Integration of Intelligence 2

Horizontal Integration “Horizontally integrating warfighter intelligence data … requires access (including discovery, search, retrieval, and display) to intelligence data among the warfighters and other producers and consumers via standardized services and architectures. These consumers include, but are not limited to, the combatant commands, Services, Defense agencies, and the Intelligence Community.” Chairman of the Joint Chiefs of Staff Instruction J2 CJCSI A 1 August 2011

Challenges to the horizontal integration of Intelligence Data Quantity and variety – Need to do justice to radical heterogeneity in the representation of data and semantics Dynamic environments – Need agile support for retrieval, integration and enrichment of data Emergence of new data resources – Need in agile, flexible, and incremental integration approach

Horizontal integration =def. multiple heterogeneous data resources become aligned in such a way that search and analysis procedures can be applied to their combined content as if they formed a single resource

This 6

7 will not yield horizontal integration

Strategy Strategy to avoid stovepipes requires a solution that is – Stable – Incrementally growing – Flexible in addressing new needs – Independent of source data syntax and semantics The answer: Semantic Enhancement (SE), a strategy of external (arm’s length) alignment

Distributed Common Ground System–Army (DCGS-A) Semantic Enhancement of the Dataspace on the Cloud Dr. Tatiana Malyuta New York City College of Technology of the City University of New York

Dataspace on the Cloud Salmen, et al,. Integration of Intelligence Data through Semantic Enhancement, STIDS 2011Integration of Intelligence Data through Semantic Enhancement strategy for developing an SE suite of orthogonal reference ontology modules Smith, et al. Ontology for the Intelligence Analyst, CrossTalk: The Journal of Defense Software Engineering November/December 2012,18-25.Ontology for the Intelligence Analyst, CrossTalk: The Journal of Defense Software Engineering November/December 2012,18-25 Shows how SE approach provides immediate benefits to the intelligence analyst

Dataspace on the Cloud Cloud (Bigtable-like) store of heterogeneous data and data semantics – Unified representation of structured and unstructured data – Without loss and or distortion of data or data semantics Homogeneous standardized presentation of heterogeneous content via a suite of SE ontologies Heterogeneous Contents SE ontologies User

Dataspace on the Cloud Cloud (Bigtable-like) store of heterogeneous data and data semantics – Unified representation of structured and unstructured data – Without loss and or distortion of data or data semantics Homogeneous standardized presentation of heterogeneous content via a suite of SE ontologies Heterogeneous Contents SE ontologies User Index

Basis of the SE Approach SE ontology labels Focusing on the terms (labels, acronyms, codes) used in the source data. Where multiple distinct terms {t 1, …, t n } are used in separate data sources with one and the same meaning, they are associated with a single preferred label drawn from a standard set of such labels All the separate data items associated with the {t 1, … t n } thereby linked together through the corresponding preferred labels. Preferred labels form basis for the ontologies we build Heterogeneous Contents ABC KLM XYZ

SE Requirements to achieve Horizontal Integration The ontologies must be linked together through logical definitions to form a single, non- redundant and consistently evolving integrated network The ontologies must be capable of evolving in an agile fashion in response to new sorts of data and new analytical and warfighter needs  our focus here

Creating the SE Suite of Ontology Modules Incremental distributed ontology development – based on Doctrine; – involves SMEs in label selection and definition Ontology development rules and principles – A shared governance and change management process – A common ontology architecture incorporating a common, domain-neutral, upper-level ontology (BFO) An ontology registry A simple, repeatable process for ontology development A process of intelligence data capture through ‘annotation’ or ‘tagging’ of source data artifacts Feedback between ontology authors and users

Intelligence Ontology Suite No.Ontology PrefixOntology Full NameList of Terms 1AOAgent Ontology 2ARTOArtifact Ontology 3BFOBasic Formal Ontology 4EVOEvent Ontology 5GEOGeospatial Feature Ontology 6IIAOIntelligence Information Artifact Ontology 7LOCOLocation Reference Ontology 8TARGOTarget Ontology HomeIntroductionPMESII-PTASCOPEReferencesLinks Welcome to the I2WD Ontology Suite! I2WD Ontology Suite: A web server aimed to facilitate ontology visualization, query, and development for the Intelligence Community. I2WD Ontology Suite provides a user-friendly web interface for displaying the details and hierarchy of a specific ontology term. 16

Ontology Development Principles Reference ontologies – capture generic content and are designed for aggressive reuse in multiple different types of context – Single inheritance – Single reference ontology for each domain of interest Application ontologies – created by combining local content with generic content taken from relevant reference ontologies

Illustration 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 Definitions

Illustration Vehicle Tractor Wheeled Tractor Artillery Tractor Wheeled Artillery Tractor Artillery Vehicle Black – reference ontologies Red – application ontologies

Role of Reference Ontologies Normalized (compare Ontoclean) – Allows us to maintain a set of consistent ontologies – Eliminates redundancy Modular – A set of plug-and-play ontology modules – Enables distributed development Surveyable – Common principles used, common training and governance

Examples of Principles All terms in all ontologies should be singular nouns Same relations between terms should be reused in every ontology Reference ontologies should be based on single inheritance All definitions should be of the form an S = Def. a G which Ds where ‘G’ (for: species) is the parent term of S in the corresponding reference ontology

SE Architecture The Upper Level Ontology (ULO) in the SE hierarchy must be maximally general (no overlap with domain ontologies) The Mid-Level Ontologies (MLOs) introduce successively less general and more detailed representations of types which arise in successively narrower domains until we reach the Lowest Level Ontologies (LLOs). The LLOs are maximally specific representation of the entities in a particular one-dimensional domain

Architecture Illustration

Intelligence Ontology Suite No.Ontology PrefixOntology Full NameList of Terms 1AOAgent Ontology 2ARTOArtifact Ontology 3BFOBasic Formal Ontology 4EVOEvent Ontology 5GEOGeospatial Feature Ontology 6IIAOIntelligence Information Artifact Ontology 7LOCOLocation Reference Ontology 8TARGOTarget Ontology HomeIntroductionPMESII-PTASCOPEReferencesLinks Welcome to the I2WD Ontology Suite! I2WD Ontology Suite: A web server aimed to facilitate ontology visualization, query, and development for the Intelligence Community. I2WD Ontology Suite provides a user-friendly web interface for displaying the details and hierarchy of a specific ontology term. 24

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*) Extension Strategy + Modular Organization 25 top level mid-level domain level Information Artifact Ontology (IAO) Ontology for Biomedical Investigations (OBI) Spatial Ontology (BSPO) Basic Formal Ontology (BFO)

Shared Semantic Resource Growing collection of shared ontologies asserted and application Pilot program to coordinate a small number of development communities including both DSC (internal) and external groups to produce their ontologies according to the best practice guidelines of the SE methodology

Given the principles of building the SE (governance, distributed incremental development, common architecture) the next step is to create a semantic resource that can be shared by a larger community, and used for inter- and intra-integration on numerous systems Heterogeneous Contents Shared Semantic Resource Dataspace Army Navy Air Force

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29 MILITARY OPERATIONS ONTOLOGY SUITE

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*) Extension Strategy + Modular Organization 30 top level mid-level domain level Information Artifact Ontology (IAO) Ontology for Biomedical Investigations (OBI) Spatial Ontology (BSPO) Basic Formal Ontology (BFO)

continuant independent continuant portion of material object fiat object part object aggregate object boundary site dependent continuant generically dependent continuant information artifact specifically dependent continuant quality realizable entity function role disposition spatial region 0D-region 1D-region 2D-region 3D-region BFO:continuant 31

occurrent processual entity process fiat process part process aggregate process boundary processual context spatiotemporal region scattered spatiotemporal region connected spatiotemporal region spatiotemporal instant spatiotemporal interval temporal region scattered temporal region connected temporal region temporal instant temporal interval BFO:occurrent 32

Conclusion

Acknowledgements