Leveraging Semantic Web Techniques to Gain Situational Awareness

Transcription

1 Wright State University CORE Scholar Kno.e.sis Publications The Ohio Center of Excellence in Knowledge- Enabled Computing (Kno.e.sis) Leveraging Semantic Web Techniques to Gain Situational Awareness Amit P. Sheth Wright State University - Main Campus, amit.sheth@wright.edu Follow this and additional works at: Part of the Bioinformatics Commons, Communication Technology and New Media Commons, Databases and Information Systems Commons, OS and Networks Commons, and the Science and Technology Studies Commons Repository Citation Sheth, A. P. (2007). Leveraging Semantic Web Techniques to Gain Situational Awareness.. This Presentation is brought to you for free and open access by the The Ohio Center of Excellence in Knowledge-Enabled Computing (Kno.e.sis) at CORE Scholar. It has been accepted for inclusion in Kno.e.sis Publications by an authorized administrator of CORE Scholar. For more information, please contact corescholar@

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3 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, Amit Sheth LexisNexis Ohio Eminent Scholar Kno.e.sis Center Wright State University Thanks: Cory Henson and Sensor Data Management team (M. Perry, S. Sahoo)

4 Outline 1. Situational Awareness (SA) 2. SA within the Semantic Web Situation Awareness (SAW) Ontology Sensor Web Enablement Provenance Context Spatial-Temporal-Thematic Analysis

5 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).

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

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

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

9 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

10 Sensor Web Enablement

11 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

12 Sensor Web Enablement Constellations of heterogeneous sensors Vast set of users and applications Satellite Airborne Sensor Web Enablement Weather Surveillance Chemical Detectors Biological Detectors Sea State Distributed self-describing sensors and related servicesnetwork Services Link sensors to network and networkcentric 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

13 SWE Languages and Encodings Information Model for Observations and Sensing Sensor and Processing Description Language Observations & Measurements (O&M) SensorML (SML) GeographyML (GML) TransducerML (TML) 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.

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

15 Situation Awareness Ontology

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

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

18 Provenance Context

19 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

20 Spatial, Temporal, Thematic Analysis

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

22 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

23 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 RDF 3 to encode temporal properties of relationships Demonstrate expressiveness with various query operators built upon thematic contexts Graph Pattern queries over spatial and temporal RDF data 2 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 1. 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, 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, Claudio Gutiérrez, Carlos A. Hurtado, Alejandro A. Vaisman. Temporal RDF, ESWC 2005:

24 Upper-level Ontology modeling Theme and Space Occurrent Continuant Spatial_Occurrent Named_Place Dynamic_Entity occurred_at located_at Spatial_Region rdfs:subclassof property Occurrent: Events happen and then don t exist Continuant: Named_Place: Spatial_Occurrent: Spatial_Region: occurred_at: Concrete Those Links Records and entities Spatial_Occurents Events Abstract exact with with concrete spatial static Entities location spatial to their spatial persist behavior locations (geometry geographic over (e.g. time (e.g. objects, locations building) a speech) Dynamic_Entity: located_at: Links Those Named_Places entities with dynamic to their coordinate geographic spatial behavior system locations (e.g. info) person)

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

26 Sample STT Query Scenario (Biochemical Threat Detection): Analysts must examine soldiers symptoms to detect possible biochemical attack Query specifies (1) a relationship between a soldier, a chemical agent and a battle location (graph pattern 1) (2) a relationship between members of an enemy organization and their known locations (graph pattern 2) (3) a spatial filtering condition based on the proximity of the soldier and the enemy group in this context (spatial Constraint)

27 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

28 References C. Matheus, M. Kokar and K. Baclawski, A Core Ontology for Situation Awareness, Sixth International Conference on Information Fusion, pp , Cairns, Australia, July 2003 C. Matheus, M. Kokar, K. Baclawski and J. Letkowski, An Application of Semantic Web Technologies to Situation Awareness, 4 th 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: /j.inffus 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, Open Geospatial Consortium, Sensor Web Enablement WG, Sam Bacharach, GML by OGC to AIXM 5 UGM, OGC, Feb. 27, 2007.