Experiences with using OWL in Military Applications
Lee W. Lacy
1
, Gabriel Aviles
1
, Karen Fraser
1
, William Gerber, PhD
1
,
Alice Mulvehill, PhD
2
, ITC (SW) Robert Gaskill
3
1
Dynamics Research Corporation, Suite 100, 3505 Lake Lynda Drive, Orlando, Florida, 32817
USA
{llacy, gaviles, kfraser, wgerber}@DRC.com
http://www.DRC.com
2
BBN Technologies, 10 Moulton Street, Cambridge, Massachusetts 02138 USA
amm@BBN.com
3
JEOD - KTOD -ACTD, Naval Explosive Ordnance Disposal Technology Division, 2008
Stump Neck Road, Indian Head, Maryland, 20640-5070 USA
Robert.J.Gaskill@jeodnet.mil
Abstract. The United States military is continuing to research and is beginning
to employ OWL-related information representation technologies. The Joint
Explosive Ordnance Disposal (JEOD) Decision Support System (DSS) is using
OWL to present relevant procedural information to warfighters. The United
States Air Force (USAF) is experimenting with representing portions of the
Foreign Clearance Guide (FCG) in OWL to support automated planning for
transportation missions. The Defense Modeling and Simulation Office
(DMSO) is experimenting with representing Computer Generated Forces (CFG)
behaviors in OWL. These efforts have yielded “lessons learned” that can
support future implementations of OWL-related technologies.
1
Introduction
Military organizations in the United States are beginning to recognize
the potential for semantic markup of information. As with any new
technology, appropriate applications of OWL must be identified (Lacy,
2005). Several efforts have been undertaken to research the benefits of
marking up military information using the Web Ontology Language –
OWL. These efforts include the:
•
Joint Explosive Ordnance Disposal (JEOD) Decision Support
System (DSS),
•
Foreign Clearance Guide (FCG), and
•
Computer Generated Forces (CFG) Human Behavior
Representation (HBR).
2 Lee W. Lacy1, Gabriel Aviles1, Karen Fraser1, William Gerber, PhD1, Alice
Mulvehill, PhD2, ITC (SW) Robert Gaskill3
2
JEOD Decision Support System (DSS)
The military’s Explosives Ordnance Disposal (EOD) mission includes
rendering safe and disposing of any explosive material including the
Improvised Explosive Devices (IEDs) that have caused many casualties
in Afghanistan and Iraq. The United States military is experimenting
with providing new advanced technologies to EOD technicians through
a Joint EOD Advanced Concept Technology Demonstration (ACTD).
The ACTD is responsible for a new JEOD Decision Support System
(JEOD DSS) that includes a network infrastructure (JEODNET), a web
portal (JEOD Portal), and the JEOD Mobile Field Kit (MFK).
2.1
JEOD Information Representation Challenge
One function of the JEOD DSS MFK is to provide Tactics, Techniques,
and Procedures (TTP) information. One goal of the system is to
present only relevant procedures to warfighters based on conditions and
measures as they pertain to the warfighter’s current environment. This
TTP information has been historically structured as a hierarchical data
set. By associating certain tasks and steps with conditions (e.g., time of
day, weather conditions), information can be tailored to support the
user’s view and environment before presentation (Meeks, 2004)
(Aviles, 2005).
Another challenge is to format the desired information in a manner that
is form factor and operating system independent. (e.g., tablets and or
laptops running any mainstream operating system) and to present the
information only to authorized users. EOD TTP includes sensitive
information. Some information is classified, and even the unclassified
content has associated releasability restrictions or caveats.
2.2
JEOD’s OWL Solution
The JEOD DSS used OWL to define an ontology for TTP and an
ontology for conditions. TTP content was then marked up in
RDF/XML to comply with these ontologies. Consuming applications
used the TTP markup to provide functionality to users. The OWL
representational ontology for procedures (i.e., TTP) includes support
Experiences with using OWL in Military Applications 3
for hierarchies of tasks, steps, and substeps that can have multiple
forms of associated media and conditions. As the environment
changes, TTP can be accessed and navigated in a non-linear fashion,
unlike standard branching navigation usually associated with
hierarchical data sets. This functionality is supported by a conditions
domain ontology that describes conditions (e.g., “precipitation”) and
extensible values (e.g., “raining”).
A Content Authoring Tool (CAT) was developed as part of the JEOD
Portal to allow subject matter experts (SMEs) to author TTP content
and associate the content with conditions. A JSR168-compliant portlet
was developed to provide automated markup of content using the
JEOD Portal. The CAT provides a user interface and an underlying
Oracle database to capture TTP and relevant descriptions. TTP content
is then exported into RDF/XML instance files that are compliant with
the TTP ontology.
At present, the primary JEOD DSS software consumer of the marked
up content is the Reference Assistant Tool (RAT) which is a plug in to
the JEOD DSS MFK. However, JEOD plans to begin leveraging
ontologies within other MFK and portal tools. JEOD is starting to
migrate additional legacy information into an environment that is richly
tagged and ontologically driven. JEOD also plans to use ontologies to
drive data federation services. Conditions associated with TTP content
are used for filtering and determining content relevance. This is
accomplished using XSLT routines that process RDF/XML-encoded
TTP content based on real-time conditions. Users can over-ride current
system generated or sensor driven conditions in order to trigger a real-
time re-publishing of the content.
2.3
JEOD Implementation Analysis
The CAT was critical for marking up TTP content. It made the
RDF/XML syntax and associated TTP ontology transparent to the
authors of the content. The TTP ontology is a representational
ontology. Great value could be achieved by linking some of the
marked up content with domain ontologies. For example, procedures
that reference specific IED components could be linked to the IED
ontology originally developed for another portion of the JEOD DSS.
4 Lee W. Lacy1, Gabriel Aviles1, Karen Fraser1, William Gerber, PhD1, Alice
Mulvehill, PhD2, ITC (SW) Robert Gaskill3
XSLT routines perform run-time semantic-based filtering and
formatting of RDF content. Eventually, inferencing routines capable of
processing against multiple linked ontologies will provide more
sophisticated functionality such as searching across domains to
aggregate information.
3
Foreign Clearance Guide (FCG)
The United States Air Force’s Air Mobility Command (AMC)
recognized planning problems related to diplomatic clearances
(Stedman, 2005). Rules regarding diplomatic clearances are described
in a text document called the Foreign Clearance Guide (FCG) that is
targeted at human readers. Currently, AMC mission planners manually
calculate lead times, calendar constraints, and country restrictions to
determine diplomatic clearance viability.
3.1
FCG Information Representation Challenge
This manual approach has led to problems. Each day, AMC loses over
$80K in fuel and one sortie daily due to diplomatic clearance
violations. This results in a loss of over $100K per day during
contingencies. Indirect effects of these problems include:
•
Lost crew time,
•
Delays in transportation/supply system, and
•
Disrupted flight/cargo movement schedules.
Air Force researchers recognized that making portions of the FCG
automatically consumable by planning software could reduce the
frequency of problem incidents.
3.2
ACT’s OWL Solution
A research effort was conducted to markup FCG information and to
develop a prototype software tool called the Automated Clearance Tool
(ACT) to automate some of the AMC’s processes using that
information (Mulvehill, 2004) (Mulvehill, 2005). OWL ontologies
were developed to support the representation of FCG information as
Experiences with using OWL in Military Applications 5
instance data. An Oracle database was developed to automate the
process of creating the instance data files.
ACT is a decision-support tool that uses agents to support the
processing of diplomatic clearances for Air Mobility Command
(AMC). These ACT software agents use the OWL ontologies to reason
about annotated diplomatic clearance-related data. The primary
purpose of the ACT software agents is to use annotated FCG country
data and local knowledge bases to automatically compute the amount
of lead time that each country involved in a mission will require.
Reasonable realizations of the lead time help AMC mission planners
acquire the required diplomatic clearances. However, ACT also
supports the overall diplomatic clearance process by providing services
including:
•
Processing diplomatic clearance mission requests,
•
Monitoring key events in the process,
•
Making changes to existing plans as needed, and
•
Making requests for special clearances like blanket allocation
and special clearance management easier.
In addition, ACT uses ontologies and semantic annotation to provide:
•
Data-form consistency and update,
•
Alerts to the user about environment changes (e.g., new
missions, data changes),
•
Graphical methods to display mission and/or diplomatic
clearance problems, and
•
The automatic generation of explanations of how calculations
are performed.
3.3
ACT Analysis
Much of the FCG’s content includes both contact information and
geographic information. Although existing ontologies were leveraged
to support representation of common concepts inherent in these two
domain areas, such as latitude and longitude for geographical entities
and phone number for contact information, a more standard ontology
definition for these domains would have been useful.
6 Lee W. Lacy1, Gabriel Aviles1, Karen Fraser1, William Gerber, PhD1, Alice
Mulvehill, PhD2, ITC (SW) Robert Gaskill3
Because of the intent to transition the lead time engine of ACT, the
consuming Java agents were limited to performing only the inferencing
required to support lead time computation. The notable exceptions
were the inferencing by lead time agents about hazardous cargo and
special clearances. For example, the FCG specifies what category of
cargo is allowed or restricted for landing in or over-flight of a country.
The lead time agents could use that information, and integrate it with
information about cargo categories that was contained in the hazardous
cargo brain book in order to determine if additional time was required
to obtain landing or overflight clearances for the mission involved.
Although a more extensive domain ontology for hazardous cargo would
have been useful for making inferences about cargo, the ACT agents
did use information about the hazardous cargo category codes to
modify the requirements for diplomatic clearances for that mission.
Many rules, restrictions, and exceptions are specified in the FCG. At
the time of ACT development, it was difficult to represent this type of
information types in OWL. For example, an ontology might express the
rule, “If a mission aircraft carries hazardous cargo and a country
specifies that no mission carrying hazardous cargo can land, then each
airbase associated with the country will not allow a mission carrying
hazardous cargo to land”. Because there was limited rule reasoning
support available in OWL at the time, the ACT user could create or
modify some rules through the use of local ACT knowledge sources
called “brain books”. Each of these brain books had an underlying
model that described relationships among its entities (each entity
specified through an ontology). Availability of a rules language (e.g.,
SWRL) would have drastically simplified the expression of conditions
associated with certain diplomatic restrictions.
4
Computer Generated Forces (CGF) Human Behavior
Representation (HBR)
Computer generated forces (CGF) are used to provide opposing,
friendly, and neutral forces in simulations. Historically, the software to
provide these capabilities has been hard-coded. However, new systems
are increasingly data driven. One of the largest CGF systems ever
developed is the OneSAF Objective System (OOS), and a major factor
Experiences with using OWL in Military Applications 7
in that system’s development costs is Behavior Representation (BR).
To represent behaviors, OOS uses hard-coded “primitive” behaviors
that are assembled into composite behaviors using the OOS Behavior
Composer tool. The composite behavior descriptions are represented in
an XML-based Behavior Description Language.
4.1
CGF Behavior Representation Challenge
High costs in terms of both manpower and lengthy development time
are associated with the current practice of developing new CGF
behaviors for each new simulation (Gerber & Lacy, 2004). To date,
there has been no standardization in the representation of behaviors to
allow reuse. New behaviors are typically custom developed for each
new simulation.
Along with the development of new behaviors is the resultant
requirement, also costly, for each newly developed behavior, even
within the same simulation, to go through the complete Verification,
Validation and Accreditation (VV&A) process (Gerber & Lacy, 2004).
So that portions of behavior could be reused in new behaviors without
repeating the full VV&A process, there is also the need to be able to
associate metadata to the behaviors, including such items as the
approval authority and releasability. OWL ontologies could provide a
means for standardizing the representation of the CGF behavior domain
so that the behaviors could be more readily reused in composing new
behaviors with a less extensive VV&A process needed.
CGF behaviors represent one type of information in the modeling and
simulation domain. The use of OWL for interchanging various types of
offline simulation data has been proposed (Blais, 2004). Data
Interchange Formats (DIFs) currently specified using XML Schemas
could be defined using OWL ontologies (Lacy, 2001). OWL could also
support the discrete-event simulation community (Lacy, 2004).
4.2
CGF Behavior Representation’s OWL Solution
The OOS was selected as a sample program to ground research into
developing standard ontological behavior representations to support
8 Lee W. Lacy1, Gabriel Aviles1, Karen Fraser1, William Gerber, PhD1, Alice
Mulvehill, PhD2, ITC (SW) Robert Gaskill3
composability of CGF behaviors (Lacy, 2003) (Gerber & Lacy, 2004).
The representation used primitive behaviors and composite behaviors.
The composite behaviors, composed of one or more primitive
behaviors and possibly other composite behaviors, represented the
temporal sequence of execution of the included behaviors while the
primitive behaviors referenced the hard-coded software of the
simulation to effect changes in the simulation. Both types of behaviors
have associated metadata.
The ontologies developed were:
•
A Behavior ontology to represent the behaviors, both primitive
and composite,
•
An Artifact ontology to represent associated metadata, such as
general descriptions of the behavior, versioning information,
and the VV&A records,
•
A Concept Domain Metadata ontology to capture the
representation of the entity performing the behavior, of its
relationships to other entities and organizations, and of the
conditions under which the behavior is appropriate, and
•
A Variable ontology to support the representation of variables
used by the behavior internally and as inputs and outputs.
Prototype software was also developed to demonstrate how OOS
behaviors could be composed using OWL-compliant behavior
representations. Additionally, a few behaviors from the Joint Semi-
Automated Forces (JSAF) simulation were manually created as
instance data files in an RDF/XML format committed to the developed
behavior ontologies to demonstrate the capability of those behavior
ontologies to represent behaviors from multiple simulations.
4.3
CGF Behavior Representation Analysis
The possibility of using OWL ontologies for standardizing the
representation of composable CGF behaviors that could be reusable
across simulations was demonstrated. This research demonstrated the
tradeoff between representing information (i.e., a composite behavior)
in OWL vice representing only the metadata about information (i.e., a
primitive behavior) that is represented in another format (e.g., software
Experiences with using OWL in Military Applications 9
code, image). CGF behaviors are a very narrow/specialized domain.
However, there may be an opportunity to leverage descriptions of
process-oriented behaviors from fields such as web services and
process modeling.
5
Summary Conclusions Based on Military Implementations
The military is beginning to use OWL. Although the military is
commonly an early adopter, it must also be very careful in how it
integrates new technologies because of the associated risks. A common
challenge is the sensitivity, ownership, releasability, security, and
provenance of marked-up information. This is typically associated
with instance data rather than ontologies. Metadata properties for
describing this type of information have proven invaluable.
Some pockets of the military community are more receptive to
technology insertion than others. One way to assuage concerns about
the use of OWL has been to emphasize its use of XML and describe
OWL and RDF as standards for applying XML technology. This
relates to an education and evangelism challenge that must be
overcome for OWL to achieve widespread acceptance. As OWL based
solutions evolve from performing richer search capabilities to
leveraging semantic joins and then mature to include real-time
reasoning agents, the advantages of using OWL over XML will become
more apparent. Commercial OWL-compliant tools are also needed to
convince some potential adopters of the language’s maturity.
A successful technology insertion method for OWL in the military has
been to focus first on providing an OWL “view” of some sample set of
information by developing an ontology and marking up instance data.
New functionality (e.g., inferencing) can then be demonstrated using
the samples. As more military applications begin to adopt OWL
technologies and benefit from its features, it will become easier to
overcome technology insertion obstacles and focus instead on
technological issues.
10 Lee W. Lacy1, Gabriel Aviles1, Karen Fraser1, William Gerber, PhD1, Alice
Mulvehill, PhD2, ITC (SW) Robert Gaskill3
6
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