Ontology for Intelligence, Defense and Security: Difference between revisions
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The material below derives from a tutorial delivered at the [http://stids.c4i.gmu.edu/index.php SEMANTIC TECHNOLOGY FOR INTELLIGENCE, DEFENSE, AND SECURITY] (STIDS) Conference in George Mason University, Fairfax, VA on October 22, 2012. | |||
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'''Faculty''': Barry Smith, Tanya Malyuta, William Mandrick and Lowell Vizenor | |||
'''Part I: Semantic Technology: A Basic Introduction''' (Lowell Vizenor) | |||
:[http://mediastream.buffalo.edu/Content/research/phismith/STIDS2012/1-Vizenor.wmv Video] | |||
:[http://ncor.buffalo.edu/2012/Vizenor-Semantic-Technology-2012.pptx Slides] | |||
'''Part II: Ontology for the Intelligence Community: A Basic Introduction''' | |||
:[http://ncor.buffalo.edu/2012/Ontology-Tutorial-2012.pptx Slides]''' | |||
''' | '''What is an Ontology and What is it Useful for?''' and '''Towards Ontology Coordination: A Strategy for Intelligence Ontology''' (Barry Smith) | ||
:[http://mediastream.buffalo.edu/Content/research/phismith/STIDS2012/2-Smith.wmv Video] | |||
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'''Semantic Enhancement of the DSGC-A Dataspace on the Cloud''' (Tatiana Malyuta) | |||
:[http://mediastream.buffalo.edu/Content/research/phismith/STIDS2012/3-Malyuta.wmv Video] | |||
'''Ontology for Intelligence: 1. The Role of Governance. 2. Basic Formal Ontology''' (Barry Smith) | |||
:[http://mediastream.buffalo.edu/Content/research/phismith/STIDS2012/4-Smith.wmv Video] | |||
[ | |||
'''A Strategy for Military Ontology''' (William Mandrick) | |||
:[http://mediastream.buffalo.edu/Content/research/phismith/STIDS2012/5-Mandrick.wmv Video] | |||
[ | |||
See also the [http://ncor.buffalo.edu/2012/Horizontal-Integration-of-Warfighter-Intelligence-Data.pptx slides] from our talk "[http://ontology.buffalo.edu/smith/articles/Horizontal-integration.pdf Horizontal Integration of Warfighter Intelligence Data]", presented at the STIDS conference on October 24, 2012. | |||
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== Background reading == | == Background reading == | ||
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:We describe a strategy for integration of data that is based on the idea of semantic enhancement. The strategy promises a number of benefits: it can be applied incrementally; it creates minimal barriers to the incorporation of new data into the semantically enhanced system; it preserves the existing data (including any existing data-semantics) in their original form (thus all provenance information is retained, and no heavy preprocessing is required); and it embraces the full spectrum of data sources, types, models, and modalities (including text, images, audio, and signals). The result of applying this strategy to a given body of data is an evolving Dataspace that allows the application of a variety of integration and analytic processes to diverse data contents. We conceive semantic enhancement (SE) as a lightweight and flexible process that leverages the richness of the structured contents of the Dataspace without adding storage and processing burdens to what, in the intelligence domain, will be an already storage- and processing-heavy starting point. SE works not by changing the data to which it is applied, but rather by adding an extra semantic layer to this data. We sketch how the semantic enhancement approach can be applied consistently and in cumulative fashion to new data and data-models that enter the Dataspace. | :We describe a strategy for integration of data that is based on the idea of semantic enhancement. The strategy promises a number of benefits: it can be applied incrementally; it creates minimal barriers to the incorporation of new data into the semantically enhanced system; it preserves the existing data (including any existing data-semantics) in their original form (thus all provenance information is retained, and no heavy preprocessing is required); and it embraces the full spectrum of data sources, types, models, and modalities (including text, images, audio, and signals). The result of applying this strategy to a given body of data is an evolving Dataspace that allows the application of a variety of integration and analytic processes to diverse data contents. We conceive semantic enhancement (SE) as a lightweight and flexible process that leverages the richness of the structured contents of the Dataspace without adding storage and processing burdens to what, in the intelligence domain, will be an already storage- and processing-heavy starting point. SE works not by changing the data to which it is applied, but rather by adding an extra semantic layer to this data. We sketch how the semantic enhancement approach can be applied consistently and in cumulative fashion to new data and data-models that enter the Dataspace. | ||
Barry Smith, Tatiana Malyuta, William S. Mandrick, Chia Fu, Kesny Parent, Milan Patel, '''[http://ontology.buffalo.edu/smith/articles/Horizontal-integration.pdf Horizontal Integration of Warfighter Intelligence Data. A Shared Semantic Resource for the Intelligence Community]''', ''Proceedings of the Conference on Semantic Technology in Intelligence, Defense and Security'' (STIDS), George Mason University, Fairfax, VA, October 23-25, 2012, forthcoming in CEUR. | Barry Smith, Tatiana Malyuta, William S. Mandrick, Chia Fu, Kesny Parent, Milan Patel, '''[http://ontology.buffalo.edu/smith/articles/Horizontal-integration.pdf Horizontal Integration of Warfighter Intelligence Data. A Shared Semantic Resource for the Intelligence Community]''', ''Proceedings of the Conference on Semantic Technology in Intelligence, Defense and Security'' (STIDS), George Mason University, Fairfax, VA, October 23-25, 2012, forthcoming in CEUR. (See also [http://ncor.buffalo.edu/2012/Horizontal-Integration-of-Warfighter-Intelligence-Data.pptx slides] from the STIDS conference presentation.) | ||
:We describe a strategy that is being used for the horizontal integration of warfighter intelligence data within the framework of the US Army’s Distributed Common Ground System Standard Cloud (DSC) initiative. The strategy rests on the development of a set of ontologies that are being incrementally applied to bring about what we call the ‘semantic enhancement’ of data models used within each intelligence discipline. We show how the strategy can help to overcome familiar tendencies to stovepiping of intelligence data, and describe how it can be applied in an agile fashion to new data resources in ways that address immediate needs of intelligence analysts. | :We describe a strategy that is being used for the horizontal integration of warfighter intelligence data within the framework of the US Army’s Distributed Common Ground System Standard Cloud (DSC) initiative. The strategy rests on the development of a set of ontologies that are being incrementally applied to bring about what we call the ‘semantic enhancement’ of data models used within each intelligence discipline. We show how the strategy can help to overcome familiar tendencies to stovepiping of intelligence data, and describe how it can be applied in an agile fashion to new data resources in ways that address immediate needs of intelligence analysts. | ||
Barry Smith, Tatiana Malyuta, David Salmen, William Mandrick, Kesny Parent, Shouvik Bardhan, Jamie Johnson, '''[http://ncor.buffalo.edu/mil/Ontology_for_the_Intelligence_Analyst.pdf Ontology for the Intelligence | Barry Smith, Tatiana Malyuta, David Salmen, William Mandrick, Kesny Parent, Shouvik Bardhan, Jamie Johnson, '''[http://ncor.buffalo.edu/mil/Ontology_for_the_Intelligence_Analyst.pdf Ontology for the Intelligence Analyst]''', ''CrossTalk: The Journal of Defense Software Engineering'', November/December 2012,18-25]. | ||
:As available intelligence data and information expand in both quantity and variety, new techniques must be deployed for search and analytics. One technique involves the semantic enhancement of data through the creation of what are called ‘ontologies’ or ‘controlled vocabularies.’ When multiple different bodies of heterogeneous data are tagged by means of terms from common ontologies, then these data become linked together in ways which allow more effective retrieval and integration. We describe a simple case study to show how these benefits are being achieved, and we describe our strategy for developing a suite of ontologies to serve the needs of the war-fighter in the ever more complex battlespace environments of the future. | :As available intelligence data and information expand in both quantity and variety, new techniques must be deployed for search and analytics. One technique involves the semantic enhancement of data through the creation of what are called ‘ontologies’ or ‘controlled vocabularies.’ When multiple different bodies of heterogeneous data are tagged by means of terms from common ontologies, then these data become linked together in ways which allow more effective retrieval and integration. We describe a simple case study to show how these benefits are being achieved, and we describe our strategy for developing a suite of ontologies to serve the needs of the war-fighter in the ever more complex battlespace environments of the future. | ||
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== Faculty == | == Faculty == | ||
Latest revision as of 16:00, 19 November 2012
The material below derives from a tutorial delivered at the SEMANTIC TECHNOLOGY FOR INTELLIGENCE, DEFENSE, AND SECURITY (STIDS) Conference in George Mason University, Fairfax, VA on October 22, 2012.
Faculty: Barry Smith, Tanya Malyuta, William Mandrick and Lowell Vizenor
Part I: Semantic Technology: A Basic Introduction (Lowell Vizenor)
Part II: Ontology for the Intelligence Community: A Basic Introduction
What is an Ontology and What is it Useful for? and Towards Ontology Coordination: A Strategy for Intelligence Ontology (Barry Smith)
Semantic Enhancement of the DSGC-A Dataspace on the Cloud (Tatiana Malyuta)
Ontology for Intelligence: 1. The Role of Governance. 2. Basic Formal Ontology (Barry Smith)
A Strategy for Military Ontology (William Mandrick)
See also the slides from our talk "Horizontal Integration of Warfighter Intelligence Data", presented at the STIDS conference on October 24, 2012.
Background reading
David Salmen, Tatiana Malyuta, Alan Hansen, Shaun Cronen, Barry Smith, Integration of Intelligence Data through Semantic Enhancement, Proceedings of the Conference on Semantic Technology in Intelligence, Defense and Security (STIDS), George Mason University, Fairfax, VA, November 16-17, 2011, CEUR, Vol. 808, 6-13.
- We describe a strategy for integration of data that is based on the idea of semantic enhancement. The strategy promises a number of benefits: it can be applied incrementally; it creates minimal barriers to the incorporation of new data into the semantically enhanced system; it preserves the existing data (including any existing data-semantics) in their original form (thus all provenance information is retained, and no heavy preprocessing is required); and it embraces the full spectrum of data sources, types, models, and modalities (including text, images, audio, and signals). The result of applying this strategy to a given body of data is an evolving Dataspace that allows the application of a variety of integration and analytic processes to diverse data contents. We conceive semantic enhancement (SE) as a lightweight and flexible process that leverages the richness of the structured contents of the Dataspace without adding storage and processing burdens to what, in the intelligence domain, will be an already storage- and processing-heavy starting point. SE works not by changing the data to which it is applied, but rather by adding an extra semantic layer to this data. We sketch how the semantic enhancement approach can be applied consistently and in cumulative fashion to new data and data-models that enter the Dataspace.
Barry Smith, Tatiana Malyuta, William S. Mandrick, Chia Fu, Kesny Parent, Milan Patel, Horizontal Integration of Warfighter Intelligence Data. A Shared Semantic Resource for the Intelligence Community, Proceedings of the Conference on Semantic Technology in Intelligence, Defense and Security (STIDS), George Mason University, Fairfax, VA, October 23-25, 2012, forthcoming in CEUR. (See also slides from the STIDS conference presentation.)
- We describe a strategy that is being used for the horizontal integration of warfighter intelligence data within the framework of the US Army’s Distributed Common Ground System Standard Cloud (DSC) initiative. The strategy rests on the development of a set of ontologies that are being incrementally applied to bring about what we call the ‘semantic enhancement’ of data models used within each intelligence discipline. We show how the strategy can help to overcome familiar tendencies to stovepiping of intelligence data, and describe how it can be applied in an agile fashion to new data resources in ways that address immediate needs of intelligence analysts.
Barry Smith, Tatiana Malyuta, David Salmen, William Mandrick, Kesny Parent, Shouvik Bardhan, Jamie Johnson, Ontology for the Intelligence Analyst, CrossTalk: The Journal of Defense Software Engineering, November/December 2012,18-25].
- As available intelligence data and information expand in both quantity and variety, new techniques must be deployed for search and analytics. One technique involves the semantic enhancement of data through the creation of what are called ‘ontologies’ or ‘controlled vocabularies.’ When multiple different bodies of heterogeneous data are tagged by means of terms from common ontologies, then these data become linked together in ways which allow more effective retrieval and integration. We describe a simple case study to show how these benefits are being achieved, and we describe our strategy for developing a suite of ontologies to serve the needs of the war-fighter in the ever more complex battlespace environments of the future.
Faculty
Tatiana Malyuta, PhD, is Principal Data Architect and Researcher at Data Tactics Corporation and an Associate Professor of the New York College of Technology of CUNY. She is a subject matter expert in data design and data integration. Recently she has been working on integrated data stores on the Cloud within the context of the Army's Distributed Common Ground System (DCGS-A).
William Mandrick, PhD, is a Senior Ontologist at Data Tactics Corpration and an Adjunct Professor at the University at Buffalo. He is also a Lieutenant Colonel in the U.S. Army Reserves with deployments to Iraq and Afghanistan where he has commanded soldiers, planned for major operations, and served as the primary civil-military operations advisor to a Brigade Combat Team. Recently he has been working on intelligence related ontologies for the Intelligence and Information Warfare Directorate (I2WD).
Barry Smith, PhD, is an internationally recognized leader in the field of ontology and semantic technology. He is Professor of Philosophy, Neurology, and Computer Science and Engineering at the State University of New York at Buffalo. Smith is Director of the National Center for Ontological Research, the founder of the Ontology for the Intelligence Community (now STIDS) conference series, and organizer of multiple conferences and training events in ontology and its applications.
Lowell Vizenor, PhD, is Ontology and Semantic Technology Practice Lead for Alion Science and Technology and is currently supporting the NextGen Air Transportation Joint Planning and Development Office Net-Centric Operations Division in the role of Lead Semantic Architect. He has over 10 years experience developing and implementing semantic solutions for industry, government and academia.
