Wright State UniversityCORE Scholar

Kno.e.sis Publications The Ohio Center of Excellence in Knowledge-Enabled Computing (Kno.e.sis)

11-14-2007

Leveraging Semantic Web Techniques to GainSituational AwarenessAmit P. ShethWright State University - Main Campus, amit.sheth@wright.edu

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Repository CitationSheth, A. P. (2007). Leveraging Semantic Web Techniques to Gain Situational Awareness. .http://corescholar.libraries.wright.edu/knoesis/98

Leveraging Semantic Webtechniques 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 ShethLexisNexis Ohio Eminent Scholar

Kno.e.sis CenterWright State University

http://knoesis.wright.edu

Thanks: Cory Henson and Sensor Data Management team (M. Perry, S. Sahoo)

Outline

1. Situational Awareness (SA)

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

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

JDL: Data Fusion Model

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

Collect Relevant

Data

Pr

ov

en

an

ce

Relate Situation Entities

Semantic Analysis thematic Spatio-Temporal trust

M. Kokar, et al., Ontology-based Situation Awareness* (Modified Figure)

Endsley’s Model w/ Semantics

Identify Situation Entities

Situation Awareness Data Pyramid

Sensor Data (World)

Entity Metadata (Perception)

Relationship Metadata

(Comprehension)

Data

Information

Semantics/Understanding/Insight

Data Pyramid

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

Sensor Web Enablement

OGC Mission To lead in the development,

promotion and harmonization of

open spatial standards

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.)

http://www.opengeospatial.org/projects/groups/sensorweb

Network Services

Vast set of users and applicationsConstellations of heterogeneous sensors

Weather

ChemicalDetectors

BiologicalDetectors

Sea State

Surveillance

Airborne

Satellite

• 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

Sensor Web Enablement

Sensor Web Enablement

http://www.opengeospatial.org/projects/groups/sensorweb

GeographyML (GML)

TransducerML (TML)

Observations & Measurements

(O&M)

Information Model for Observations and Sensing

Sensor and Processing Description Language

Multiplexed, Real Time Streaming Protocol

Common Model for Geography Systems and Features

SensorML (SML)

Sam Bacharach, “GML by OGC to AIXM 5 UGM,” OGC, Feb. 27, 2007.

SWE Languages and Encodings

17

Semantic Sensor ML – Adding Ontological Metadata

Person

Company

Coordinates

Coordinate System

SpatialOntology

Mike Botts, "SensorML and Sensor Web Enablement," Earth System Science Center, UAB Huntsville

DomainOntology

Event

Situation

Situation AwarenessOntology

Time Units

Timezone

TemporalOntology

Situation Awareness Ontology

Ontology

What is an Ontology?

“Ontology is about the exact description of things and their relationships.”

World Wide Web Consortium (W3C)

Situation Awareness Ontology

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

Provenance Context

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

Spatial, Temporal, Thematic Analysis

North Korea detonates nuclear device on October 9, 2006 near Kilchu, North KoreaNorth Korea detonates nuclear device October 9, 2006

Kilchu, North Korea

Thematic Dimension: What Temporal Dimension: When

Spatial Dimension: Where

Three Dimensions of Information

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

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

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, 2006

2. 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

3. Claudio Gutiérrez, Carlos A. Hurtado, Alejandro A. Vaisman. “Temporal RDF”, ESWC 2005: 93-107

Upper-level Ontology modeling Theme and Space

OccurrentContinuant

Named_PlaceSpatial_OccurrentDynamic_Entity

Spatial_Region

Occurrent: Events – happen and then don’t existContinuant: Concrete and Abstract Entities – persist over timeNamed_Place: Those entities with static spatial behavior (e.g. building)Dynamic_Entity: Those entities with dynamic spatial behavior (e.g. person)Spatial_Occurrent: Events with concrete spatial locations (e.g. a speech)Spatial_Region: Records exact spatial location (geometry objects,

coordinate system info)

occurred_at located_at

occurred_at: Links Spatial_Occurents to their geographic locationslocated_at: Links Named_Places to their geographic locations

rdfs:subClassOfproperty

OccurrentContinuant

Named_Place

Spatial_OccurrentDynamic_Entity

PersonCity

Politician

SoldierMilitary_Unit

BattleVehicle

Bombing

Speech

Military_Eventassigned_to

on_crew_ofused_in

gives

participates_in

trains_at

Spatial_Region

located_at occurred_at

Upper-level Ontology

Domain Ontology

rdfs:subClassOf used for integrationrdfs:subClassOfrelationship type

dynamic entities get spatial properties indirectly through relationships with spatial entities

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)

Using SW to enable perception and comprehension

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

Utilizing Semantic Web technologies to enable perception and comprehension within Situational Awareness

• 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.

References