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Leveraging Semantic Web techniques to gain situational awareness Can Semantic Web techniques empower perception and comp

Leveraging Semantic Web techniques to gain situational awareness Can Semantic Web techniques empower perception and comprehension in Cyber Situational Awareness? Talk at Cyber Situational Awareness Workshop, Fairfax, VA Nov 14-15, 2007. Amit Sheth LexisNexis Ohio Eminent Scholar

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Leveraging Semantic Web techniques to gain situational awareness Can Semantic Web techniques empower perception and comp

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  1. Leveraging Semantic Web techniques to gain situational awareness Can Semantic Web techniques empower perception and comprehension in Cyber Situational Awareness? Talk at Cyber Situational Awareness Workshop, Fairfax, VA Nov 14-15, 2007. Amit Sheth LexisNexis Ohio Eminent Scholar Kno.e.sis Center Wright State University http://knoesis.wright.edu Thanks: Cory Henson and Sensor Data Management team (M. Perry, S. Sahoo)

  2. Outline • Situational Awareness (SA) • SA within the Semantic Web • Situation Awareness (SAW) Ontology • Sensor Web Enablement • Provenance Context • Spatial-Temporal-Thematic Analysis

  3. Situation Awareness “Situation awareness is the perception of elements in the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future.” (1988, Mica Endsley). http://en.wikipedia.org/wiki/Situation_awareness

  4. JDL: Data Fusion Model A. Steinberg, et al., Rethinking the JDL Data Fusion Levels

  5. Endsley’s Model w/ Semantics • Semantic Analysis • thematic • Spatio-Temporal • trust Relate Situation Entities Identify Situation Entities Provenance Collect Relevant Data M. Kokar, et al., Ontology-based Situation Awareness* (Modified Figure)

  6. Data Pyramid Situation Awareness Data Pyramid Semantics/Understanding/Insight Relationship Metadata (Comprehension) Expressiveness Information Entity Metadata (Perception) Data Sensor Data (World)

  7. Situation Awareness Situation Awareness Components • Physical World: Sensor Data • Perception: Entity Metadata • Comprehension: Relationship Metadata Semantic Analysis • How is the data represented?Sensor Web Enablement • What are the antecedents of the event? Provenance Analysis • Where did the event occur?Spatial Analysis • When did the event occur?Temporal Analysis • What is the significance of the event?Thematic Analysis

  8. Sensor Web Enablement

  9. Open Geospatial Consortium • Consortium of 330+ companies, government agencies, and academic institutes • Open Standards development by consensus process • Interoperability Programs provide end-to-end implementation and testing before spec approval • Standard encodings, e.g. • GeographyML, SensorML, Observations & Measurements, TransducerML, etc. • Standard Web Service interfaces, e.g. • Web Map Service • Web Feature Service • Web Coverage Service • Catalog Service • Sensor Web Enablement Services (Sensor Observation Service, Sensor Alert Service, Sensor Process Service, etc.) OGC Mission To lead in the development, promotion and harmonization of open spatial standards http://www.opengeospatial.org/projects/groups/sensorweb

  10. Sensor Web Enablement Vast set of users and applications Constellations of heterogeneous sensors Satellite Airborne Sensor Web Enablement Weather Surveillance • Distributed self-describing sensors and related services • Link sensors to network and network-centric services • Common XML encodings, information models, and metadata for sensors and observations • Access observation data for value added processing and decision support applications • Users on exploitation workstations, web browsers, and mobile devices Network Services Biological Detectors Chemical Detectors Sea State http://www.opengeospatial.org/projects/groups/sensorweb

  11. TransducerML (TML) SWE Languages and Encodings Sensor and Processing Description Language Information Model for Observations and Sensing Observations & Measurements (O&M) SensorML (SML) GeographyML (GML) Common Model for Geography Systems and Features Multiplexed, Real Time Streaming Protocol Sam Bacharach, “GML by OGC to AIXM 5 UGM,” OGC, Feb. 27, 2007.

  12. Semantic Sensor ML – Adding Ontological Metadata Situation Awareness Ontology Event Situation Domain Ontology Company Person Spatial Ontology Coordinates Coordinate System Temporal Ontology Time Units Mike Botts, "SensorML and Sensor Web Enablement," Earth System Science Center, UAB Huntsville Timezone 17

  13. Situation Awareness Ontology

  14. Ontology What is an Ontology? “Ontology is about the exact description of things and their relationships.” World Wide Web Consortium (W3C)

  15. Situation Awareness Ontology C. Matheus, et al., An Application of Semantic Technologies to Situation Awareness

  16. Provenance Context

  17. Provenance What is Provenance? • The recording of details in a data process workflow • Trace back to where the particular data entity originated • The phenomena captured by the sensor • The sensor characteristics associated with data • What processing was done on data • Enables effective interpretation of object or event - Trust • Evaluate whether particular data entity is relevant in current situation based on its provenance • Enhanced situation comparison through use of provenance

  18. Spatial, Temporal, Thematic Analysis

  19. Three Dimensions of Information Temporal Dimension: When Thematic Dimension: What North Korea detonates nuclear device on October 9, 2006 near Kilchu, North Korea Spatial Dimension: Where

  20. Where we are, where we need to go Semantic Analytics • Searching, analyzing and visualizing semantically meaningful connections between named entities Significant progress with thematic data • Semantic associations (Rho-Operator) • Subgraph discovery • Query languages (SPARQ2L, SPARQLeR) • Data stores (Brahms) Spatial and Temporal data is critical in many analytical domains • Need to support spatial and temporal data and relationships

  21. Current Research Towards STT Relationship Analysis Modeling Spatial and Temporal data using SW standards (RDF(S))1 Upper-level ontology integrating thematic and spatial dimensions Use Temporal RDF3 to encode temporal properties of relationships Demonstrate expressiveness with various query operators built upon thematic contexts Graph Pattern queries over spatial and temporal RDF data2 Extended ORDBMS to store and query spatial and temporal RDF User-defined functions for graph pattern queries involving spatial variables and spatial and temporal predicates Implementation of temporal RDFS inferencing • Matthew Perry, Farshad Hakimpour, Amit Sheth. "Analyzing Theme, Space and Time: An Ontology-based Approach", Fourteenth International Symposium on Advances in Geographic Information Systems (ACM-GIS '06), Arlington, VA, November 10 - 11, 2006 • Matthew Perry, Amit Sheth, Farshad Hakimpour, Prateek Jain. “Supporting Complex Thematic, Spatial and Temporal Queries over Semantic Web Data", Second International Conference on Geospatial Semantics (GeoS ‘07), Mexico City, MX, November 29 – 30, 2007 • Claudio Gutiérrez, Carlos A. Hurtado, Alejandro A. Vaisman. “Temporal RDF”, ESWC 2005: 93-107

  22. Upper-level Ontology modeling Theme and Space Continuant Occurrent Dynamic_Entity Named_Place Spatial_Occurrent located_at occurred_at Spatial_Region rdfs:subClassOf property Occurrent: Events – happen and then don’t exist Continuant: Concrete and Abstract Entities – persist over time occurred_at: Links Spatial_Occurents to their geographic locations located_at: Links Named_Places to their geographic locations Named_Place: Those entities with static spatial behavior (e.g. building) Dynamic_Entity: Those entities with dynamic spatial behavior (e.g. person) Spatial_Region: Records exact spatial location (geometry objects, coordinate system info) Spatial_Occurrent: Events with concrete spatial locations (e.g. a speech)

  23. Upper-level Ontology Continuant Occurrent Named_Place Dynamic_Entity located_at occurred_at Spatial_Occurrent Spatial_Region City Person trains_at Speech gives Politician participates_in Military_Unit Bombing Military_Event Soldier assigned_to on_crew_of rdfs:subClassOf used for integration rdfs:subClassOf relationship type used_in Battle Vehicle Domain Ontology dynamic entities get spatial properties indirectly through relationships with spatial entities

  24. Sample STT Query • Scenario (Biochemical Threat Detection): Analysts must examine soldiers’ symptoms to detect possible biochemical attack • Query specifies • a relationship between a soldier, a chemical agent and a battle location (graph pattern 1) • a relationship between members of an enemy organization and their known locations(graph pattern 2) • a spatial filtering condition based on the proximity of the soldier and the enemy group in this context (spatial Constraint)

  25. Using SW to enable perception and comprehension Utilizing Semantic Web technologies to enable perception and comprehension within Situational Awareness Perception • Leveraging current research in sensor data representation found in the Sensor Web Enablement metadata languages • Using SWE languages to model sensors, processes, and data Comprehension • Extending the Sensor Web Enablement languages with semantic metadata to provide the ability to model relationships between entities • Semantic relationships provide “meaning” to objects and events within a situation • Using Situational Awareness Ontology to model situations and provide a framework for Semantic Analysis • Provenance Context provides a historical record of relevant objects and events within a situation • Spatial, Temporal and Thematic analysis provides the “where”, “when”, and “what” of objects and events within a situation

  26. References • C. Matheus, M. Kokar and K. Baclawski, A Core Ontology for Situation Awareness, Sixth International Conference on Information Fusion, pp.545-552, Cairns, Australia, July 2003 • C. Matheus, M. Kokar, K. Baclawski and J. Letkowski, An Application of Semantic Web Technologies to Situation Awareness, 4th International Semantic Web Conference, ISWC 2005, Galway, Ireland, November, 2005 • M. Kokar, C. Matheus and K. Baclawski, Ontology-based situation awareness, Informat. Fusion, 2007, doi:10.1016/j.inffus.2007.01.004 • M. Kokar, Ontology Based High Level Fusion and Situation Awareness: Methods and Tools, Presentation, Quebec, 2007 • A. Steinberg and C. Bowman, Rethinking the JDL data fusion levels, National Symposium on Sensor and Data Fusion, 2004 • Wikipedia, Situation Awareness, http://en.wikipedia.org/wiki/Situation_awareness • Open Geospatial Consortium, Sensor Web Enablement WG, http://www.opengeospatial.org/projects/groups/sensorweb • Sam Bacharach, “GML by OGC to AIXM 5 UGM,” OGC, Feb. 27, 2007.

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