Horizontal integration of warfighter intelligence data
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This document proposes a strategy for horizontally integrating warfighter intelligence data through semantic enhancement (SE). SE involves developing a suite of shared ontology modules according to common principles. This allows heterogeneous data sources to be semantically aligned so they can be accessed and analyzed as a single resource. The ontologies would be developed incrementally and flexibly to address new data and needs. The goal is to create a shared semantic resource that can integrate intelligence data across different military branches and systems.
Horizontal integration of warfighter intelligence data
- 1. 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
- 2. Horizontal Integration of Intelligence 2
- 3. 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 3340.02A 1 August 2011
- 4. 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
- 5. 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
- 6. This 6
- 7. 7will not yield horizontal integration
- 8. 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
- 9. 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
- 10. Dataspace on the Cloud Salmen, et al,. Integration of Intelligence Data through Semantic Enhancement, STIDS 2011 • 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. • Shows how SE approach provides immediate benefits to the intelligence analyst
- 11. 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 ontologiesUser
- 12. 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 ontologiesUser Index
- 13. Basis of the SE Approach SE ontology labels • Focusing on the terms (labels, acronyms, codes) used in the source data. • Where multiple distinct terms {t1, …, tn} 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 {t1, … tn} thereby linked together through the corresponding preferred labels. • Preferred labels form basis for the ontologies we build Heterogeneous ContentsABC KLM XYZ
- 14. 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
- 15. 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
- 16. Intelligence Ontology Suite No. Ontology Prefix Ontology Full Name List of Terms 1 AO Agent Ontology 2 ARTO Artifact Ontology 3 BFO Basic Formal Ontology 4 EVO Event Ontology 5 GEO Geospatial Feature Ontology 6 IIAO Intelligence Information Artifact Ontology 7 LOCO Location Reference Ontology 8 TARGO Target Ontology Home Introduction PMESII-PT ASCOPE References Links 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
- 17. 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
- 18. 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
- 20. 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
- 21. 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
- 22. 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
- 24. Intelligence Ontology Suite No. Ontology Prefix Ontology Full Name List of Terms 1 AO Agent Ontology 2 ARTO Artifact Ontology 3 BFO Basic Formal Ontology 4 EVO Event Ontology 5 GEO Geospatial Feature Ontology 6 IIAO Intelligence Information Artifact Ontology 7 LOCO Location Reference Ontology 8 TARGO Target Ontology Home Introduction PMESII-PT ASCOPE References Links 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
- 25. 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)
- 26. 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
- 27. • 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
- 28. 28
- 29. 29 MI L I TARY OPERAT I ONS ONTOLOGY SUI T E
- 30. 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)
- 33. Conclusion
- 34. Acknowledgements