Database Creation for Information Processing Methods, 

Metrics, and Models (DCIPM3) 

by Sean Murray, Frank Small, Michelle C. McVey, Kevin King, 

Christine Slocum, and Will Tanenbaum 

ARL-TN-0348 May 2009 

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Army Research Laboratory 

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ARL-TN-0348 May 2009 

Database Creation for Information Processing Methods, 

Metrics, and Models (DCIPM3) 

Sean Murray, Frank Small, Michelle C. McVey, Kevin King, 

Christine Slocum, and Will Tanenbaum 

Computational and Informational Sciences Directorate, ARL 

Approved for public release; distribution unlimited. 
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Database Creation for Information Processing Methods, Metrics, and Models 

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Sean Murray, Frank Small, Michelle C. McVey, Kevin King, Christine Slocum, 

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14. ABSTRACT 

One challenge of tactical information processing within the demanding net-centric environment is the fusion of data extracted 

from various free-form message* databases. The development of high-level data fusion products addresses this challenge; 

however, development relies heavily on the availability of large, accurate data sets. This report documents the Database 

Creation for Information Processing Methods, Metrics, and Models (DCIPM3) project that creates, from a U.S. Army 

Research Laboratory (ARL)-designated context, a notional albeit realistic detailed scenario encompassing a multitude of 

social interactions. A time ordered event list (TOEL) was created from the scenario that includes references to multiple forms

of communication†

, events, and interactions between subjects. An extensive message set that includes communication 

between subjects was derived from the TOEL. The Web Ontology Language (OWL) was used to create an ontology 

populated with information, e.g., “meet at two,” from the message set. Resource Description Framework (RDF) triples were 

extracted from this ontology. The resulting database from the extracted triples can be used by the information fusion 

community when both a known social context and a comprehensive database of messages to support that context are required. 

_________________ 

*

Free-form messages include semi-structured and unstructured verbal, digital, and textual communication. 

†

Forms of communication include face-to-face verbal communication and telephone conversations; digital communication, such as text 

messaging and instant messaging online; as well as written textual communication, such as personal letters.

15. SUBJECT TERMS

Ontology, database, information processing 

16. SECURITY CLASSIFICATION OF:

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Ann E. M. Bornstein 

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Prescribed by ANSI Std. Z39.18
iii 

Contents 

List of Figures iv

Acknowledgments v

1. Introduction 1

2. Database Development 1

2.1 Background .....................................................................................................................1 

2.2 Document Creation..........................................................................................................2 

2.2.1 Detailed Scenario ................................................................................................2 

2.2.2 Time Ordered Event List.....................................................................................2 

2.2.3 Message Set.........................................................................................................5 

2.3 Protégé-OWL for Ontology Building..............................................................................5 

2.3.1 Software Overview..............................................................................................5 

2.3.2 Ontology Objects.................................................................................................6 

2.3.3 DCIPM3 Ontology ..............................................................................................6 

2.3.4 Ontology Relationships .......................................................................................7 

2.4 RDF Triple Extraction.....................................................................................................8

3. Summary and Conclusions 10

4. References 11

List of Symbols, Abbreviations, and Acronyms 12 

Distibution List 13 
iv 

List of Figures 

Figure 1. Excerpt from detailed scenario document. ......................................................................2 

Figure 2. Example of an entry in the time ordered event list..........................................................3 

Figure 3. Flow chart illustrating relationship between Kevin and Megan......................................4 

Figure 4. Example of a standard message from the message set....................................................5 
v 

Acknowledgments 

The authors wish to give sincere thanks to Mr. Mark Thomas, Chief, Tactical Information 

Fusion Branch, for assigning us the Database Creation for Information Processing Methods, 

Metrics, and Models (DCIPM3) project to develop and explore. Thanks also to Mrs. Ann E. M. 

Bornstein and Dr. John Brand for their continuous support and guidance throughout the project 

and to Mr. John Richardson for his direction and input through the progression of the project. 
vi 

INTENTIONALLY LEFT BLANK. 
1 

1. Introduction 

The tactical information processing (IP) of messages in a net-centric* environment presents the 

unique challenge of fusing data extracted from various free-form message†

 databases. Currently, 

the issue of data fusion is being addressed through the development of service-orientedarchitectures (SOA) (2). SOA technology to identify information within free-form messages and 

insert that information into ontological structures remains in its infancy (3). There is still a need 

for the development of high-level fusion products to overcome the data fusion challenge. 

Development relies heavily on the availability of large, accurate, unclassified data sets that are 

difficult to obtain. The existence, access, and veracity of needed data sets are highly limited. 

Data sets that are available cannot be considered accurate, as the data is not supported by readily 

available ground truth information (4); data sets are also often limited in social interactions. 

Accurate social interaction threads are crucial to the development of many high-level fusion 

products, and a data set incorporating these threads must be developed. 

The objective of the Database Creation for Information Processing Methods, Metrics, and 

Models (DCIPM3) project was to develop a large data set involving social interactions and to 

create the materials needed to support the evaluation of knowledge discovery tools. The data 

set’s inception lay in a ubiquitous scenario—that of 15 high school students’ senior year at a 

notional high school. From the scenario, a time-ordered event list (TOEL) and a message set 

were generated. The data was then manually mined from the message set and inserted into an 

ontology created using Protégé (5). 

2. Database Development 

2.1 Background 

The Soft Target Exploitation and Fusion (STEF) Army Technology Objective (ATO) (6) was 

created to develop analytical software services that track an individual and determine his or her 

sphere of influence. Specifically, locations, times, and interactions can be determined. The 100 

Human Intelligence (HUMINT) unclassified message set (7), developed to support the STEF 

ATO, forms the template for the DCIPM3 project. 

 *

 Net-centric refers to “a framework for full human and technical connectivity and interoperability that allows all DID users 

and mission partners to share the information they need” (1). 

†

Free-form messages include semi-structured and unstructured verbal, digital, and textual communication. 
2 

To ensure the accuracy of inferences drawn from the data analysis tools, the DCIPM3 team 

divided into two groups: the scenario development group and the ontology/Resource Description 

Framework (RDF) triple group. The scenario development group developed and documented the 

scenario; the ontology/RDF triple group populated a Web Ontology Language (OWL) ontology 

from the scenario. 

2.2 Document Creation 

2.2.1 Detailed Scenario 

Given an ARL-designated context, a realistic, detailed scenario encompassing a multitude of 

social interactions was created. The scenario served as the ground truth for the message set and 

was based on a realistic notional high school. The interactions of 15 notional subjects were 

followed throughout their senior academic year, allowing for multiple social threads. Rather 

than using the typical pragmatic scenario of tactical environments, with which the DCIPM3 team 

had little experience, the high school setting allowed for a more accurate scenario, as the 

DCIPM3 team was comprised of high school and undergraduate student interns. 

The scenario, created first, established the background for the message set, and included a brief 

overview of the town demographic, a summary of the most pivotal events, and in-depth 

narrations for the academic year. The detailed scenario provides an extensive account for each 

event. Entries include actions, thoughts, and feelings of the notional characters. An excerpt 

from the detailed scenario document can be found in figure 1. 

Figure 1. Excerpt from detailed scenario document. 

2.2.2 Time Ordered Event List 

A TOEL was created to maintain a homogenous flow of events during the high school year. The 

timeline and messages from the HUMINT message set served as guidelines for crafting the 

TOEL. The TOEL breaks the scenario into the individual forms of communication, events, and 

interactions between subjects. Each event in the TOEL includes a time stamp, date, and location. 

Along with each stamp is a brief description of the primary event that transpired. Events possess 

On Thursday August 28 Mr. Brown announces to the homeroom that the pep 

rally will be the following Friday. Mike and Kevin talk about last years pep 

rally where the principal got soaked in sloppy Joe meat by the football team. 

Megan brings up the stunts of the cheerleaders last year, and how this year 

the squad is superior. Joy, Steve, and Julie, remained silent and took little 

interest in the announcement, and even looked upset. They do not enjoy big 

social events with the school yelling, and acting foolish. Later that day 

Megan, who is in Student Government Association (SGA), attends a meeting 

that addresses the lineup of events to have at the pep rally, with other 

representatives. Megan pushes to have more time for her cheerleading squad 

to perform in front of the student body. 
3 

varying levels of significance to each subject’s development, e.g., from a notional character 

daydreaming in a class to the degradation of a once strong relationship. An example entry from 

the TOEL is presented as figure 2. 

Figure 2. Example of an entry in the time ordered event list. 

While many of the documented events had some level of importance to the development of 

similar sequential events of a notional subject, many dead ends and random events were also 

developed and included to allow for the additional exploration of database analysis tools under 

these circumstances. 

Flowcharts were created from the TOEL to provide a visual representation of how events are 

linked together in threads. The flowchart shown in figure 3 illustrates the events and relationship 

between two individuals, Kevin and Megan. 

4th day 

8/28/08: Thursday 

Homeroom- Mr. Brown talks about the pep rally. It brings up memories for 

Kevin, Megan, and Mike about last years pep rally. Joy, Steve, and Julie are 

not as excited. 

After school- Student Government Association meets, to discuss the pep 

rally. Megan asks for an extended time slot for the cheerleading squad to 

perform during the pep rally. 
4 

Figure 3. Flow chart illustrating relationship between Kevin and Megan. 

School Begins

Kevin and M egan have been 

dating and do a lot of things 

together

M egan is a cheerleader and 

Kevin is a football player

See each other everyday 

afterschool because of practice

Pep Rally is to be 

held 9/5/08

W ill shanks Kevin at Pep Rally in 

front of everyone and M egan 

laughs. Kevin stops speaking to 

Megan for the day

Kevin sees M egan 

flirting with M ike 

and Megan 

sm oothes things 

over with Kevin 

afterwards

Kevin and M egan argue 

about the Pep Rally

W ill and M egan 

flirt on the phone

M egan sprains ankle 

at football gam e and 

Kevin helps her to 

the hospital to get X -

rays

W ill and M egan 

get stuck in the 

elevator during a 

big storm

Hom ecom ing is 

announced for 10/18/08 

M egan and Kevin go to 

Hom ecom ing together

Kevin and M egan 

go to Christine’s 

Hom ecom ing after 

party

Kevin and M egan 

go to a M etallica 

concert with friends 

and Kevin gets hit 

in the nose in a 

m osh pit

Kevin goes to 

M egan’s house 

and they start 

talking about W ill 

and Kevin leaves 

angered

Kevin and M egan 

attend W restling 

m atch with friends

Kevin and M egan 

go to Basketball 

game together

Kevin and M egan 

go to Ravens 

gam e with som e 

friends

Megan goes to 

Kevin’s house to 

see if he doing 

well since Kevin is 

extrem ely sick

M egan and Kevin 

go sledding with 

som e friends 

because school is 

out for snow

Kevin takes 

M egan out to TGI 

Fridays

W ill visits M egan 

at her house while 

C hristine is there 

studying

Kevin and M egan 

go bowling with 

som e friends

W ill goes to 

M egan’s house 

when Kevin and 

his friends are 

playing poker

W ill gets out of 

class to go to the 

nurse but instead 

goes to M egan’s 

history class

Kevin asks M egan 

to be his date for 

Valentines day

Megan turns down 

W ill when he asks 

M egan to be his 

date for Valentines 

day

Kevin and M egan 

go out for dinner 

with som e friends 

then spend the 

rest of the night 

alone

W ill buys M egan 

flowers for 

Valentine’s day

W ill and M egan 

hangout for the 

day and Ashley 

sees W ill enter 

M egan’s house

M egan and Kevin 

attend som e baseballs 

som etim es with friends 

and others alone

Ashley tells Kevin 

she saw W ill enter 

M egan’s house a 

few weeks back

Kevin goes to his 

cousins birthday 

party and brings 

Megan

Prom is 

announced 

for 5/16/09

W ill sees M egan 

talking to W ill in 

the hallway

Kevin sees W ill 

and Megan 

whispering during 

homeroom

Kevin sees W ill and M egan 

talking at a baseball gam e 

and Kevin takes M egan to 

dinner at that m om ent

W ill asks M egan to 

prom and she turns 

him down

M egan agrees to 

be Kevin’s date to 

prom

Megan has 18th birthday and W ill 

buys her a necklace as so does 

Kevin. Kevin takes W ill outside 

to tell him to back off M egan

Kevin sees W ill 

dancing with 

M egan and Kevin 

punches W ill and it 

breaks out into a 

big fight

M egan does not 

talk to Kevin or 

W ill the rest of the 

night

Megan breaks her 

relationship off 

with Kevin
5 

2.2.3 Message Set 

An extensive message set was produced from the TOEL and scenario. This task included the 

creation of semi-structured and unstructured free form messages. Each message entry involved 

at least one of the notional characters, as well as various forms of communication with other 

individuals.‡ A standard entry included the date, location, and type of communication, followed 

by the conversation itself. An example of a standard message can be viewed in figure 4. 

Figure 4. Example of a standard message from the message set. 

2.3 Protégé-OWL for Ontology Building 

2.3.1 Software Overview 

“Protégé is a free, open-source platform that provides...a suite of tools...[for] the creation, 

visualization, and manipulation of ontologies (5).” Protégé-OWL is an editor for the Protégé 

platform that enables users to build ontologies in the OWL, a World Wide Web Consortium 

(W3C) standard for Semantic Web applications. Formal semantics specified by OWL allow an 

ontology’s logical consequences to be derived in knowledge discovery applications. 

 ‡

Forms of communication include verbal communication; digital communication such as text messaging and instant 

messaging online; as well as written textual communication such as personal letters. 

4th day 

8/28/08: Thursday 

Homeroom- 08:50:33 Conversation 

Mr. Brown- There will be a pep rally on 9/5/08; everyone must attend, and try to 

not get to out of control. 

Mike- Remember last year? 

Kevin- Ya’ mean when, seniors dumped sloppy joe stuff on Principal Thomas?! 

Mike- Yeah! Sloppy Joes are awesome. 

Megan- Sloppy Joes are gross. Even still, the sloppy joes weren’t as bad as last 

year’s cheerleaders... 

Mike- Hoho! Harsh! 

Megan- Seriously though... Our cheer: WAY better. 

SGA meeting- 15:02:43 Conversation 

Megan- The cheer leaders will need a 5 minute block to perform. 

Marge- 5 minutes! Is that really necessary? 

Megan- That is how long our dance is! 

Marge- Fine...moving on. 
6 

OWL was selected for modeling the high school scenario to ensure compatibility with 

conventional reasoners and for its capability to determine implied and explicit relations. The 

OWL ontology is in a format that will optimize knowledge discovery opportunities and facilitate 

rapid integration into diverse analysis programs. 

2.3.2 Ontology Objects 

Ontologies are comprised of class and property definitions and are populated by class instances, 

called “individuals.” In Protégé-OWL, abstract class Thing is the superclass§ of all other 

classes; all user-defined classes must derive—as children or further descendants—from Thing. 

Subclasses inherit all parent properties and usually define additional, unique properties; inherited 

properties may be overridden by explicit subclass redefinitions. 

To illustrate the concept of class property inheritance, consider a class Vehicle for which 

properties capacity and position are defined and let two subclasses, Automobile and 

Aircraft, extend Vehicle. The Vehicle class definition specifies capacity as an integer 

referring to the maximum number of human occupants the vehicle can accommodate and 

specifies position to be a floating-point triple of the format (latitude, longitude, altitude). 

Automobile inherits capacity and position properties from its parent class but, because the 

vehicle type is restricted to land movement, Automobile redefines position as a floatingpoint double of the format (latitude, longitude) for more appropriate two-dimensional 

positioning. Aircraft does not redefine position or capacity but defines the additional 

property tilt. 

Protégé-OWL supports object and datatype property definitions. Object properties express 

relations between two class individuals (e.g., X.next_to(Y)), while datatype properties are 

literal values of instance attributes (e.g., age = 26, female = True, and female = 'John 

Doe'). Inverse properties may also be defined for all object properties, assigning reciprocal 

meanings to the receiving objects. When the object property of an individual is defined and 

there exists an inverse to the property, the inverse is automatically defined for the recipient (e.g., 

for individuals Mary and Suzie, Mary.motherOf(Suzie) Suzie.daughterOf(Y)). 

2.3.3 DCIPM3 Ontology 

A schema was designed for the DCIPM3 ontology to ensure consistent, logical, and complete 

representation of all system dynamics detailed in the project documents. Central to the schema is 

Thing subclass Event, which has the fully qualified class path Thing::Event and cannot be 

directly instantiated. Event has instantiative children TravelEvent, FinancialEvent, 

SportsEvent, HealthEvent, BandEvent, LeisureEvent, SchoolEvent, 

EmploymentEvent, CommunicationEvent, and ConflictEvent, many of which have their 

 §

A superclass, or parent class, is that from which a subclass derives. Subclasses, also called children classes, are said to 

“extend” the superclasses. 
7 

own subclasses. Abstract classes Thing::Person, Thing::Equipment, 

Thing::Organization, and Thing::Location are also defined in the schema with 

instantiative subclasses. 

During database population, a Thing::Event::*** individual is created in the DCIPM3 

ontology for each item in the message set. Datatype properties hasStartDate and 

hasStartTime are defined for each individual, as well as class-specific object properties, each 

of which relates the event instance to another individual. Properties may be restricted to certain 

datatypes, allowed values, or user-defined class instances. 

Consider WillProposition, a Thing::Event::Communication::Conversation:: 

PhoneCall individual from the DCIPM3 ontology. From the given class path, one can infer 

that WillProposition was a telephone call and, more generally, a conversation, as well as a 

means of communication. PhoneCall WillProposition denotes the event’s class 

derivation. 

For class Thing::Person::SchoolPerson::StudentPerson, let StudentPerson

MeganCrocker, WillAnderson. WillProposition properties include hasStartTime = 

12:22:03, hasStartDate = September 6, 2008, hasParticipant(MeganCrocker), 

hasParticipant(WillAnderson), and hasTopic(MeganCrocker). hasStartTime and 

hasStartDate are restricted to single values of the time and date types, respectively. 

hasParticipant is an object method with multiple allowed values and a range restricted to 

Thing::Person::* individuals. hasTopic, also an object property, may only have one value 

of a Thing::Person::* individual. 

2.3.4 Ontology Relationships 

Individuals stored in the ontology are models of data entities with respect to the larger context in 

which they exist. Information is explicitly asserted for each entity, describing individual 

characteristics and the individual’s direct relationships to other entities in the ontology. Upon 

direct examination of a given individual, the entity’s properties appear much as they would in a 

relational database. Unlike relational data representations, however, an ontology also reveals the 

extended and implicit relationships between individuals. 

Every individual is an instance of a particular class and all classes relate to each other by some 

degree of hierarchical removal, having all derived somehow from Thing. Furthermore, class 

definitions restrict object properties to finite domains and ranges, based on realistic limitations of 

the data entities being represented. So, object property values are indicators of how each 

individual relates to the system.†† The property values of one individual may impact the 

meaning of many other individuals in the ontology, even if the entities are unrelated explicitly. 

 **A fully qualified class path with a terminating asterisk denotes an arbitrary class descendant. For example, any class 

descending from Person can be expressed as Thing::Person::*. 

††The global domain of an ontology is known as the system, which encompasses all individuals of every class. 
8 

Implicit Knowledge: Example Case Study 

For example, consider an ontology with class definitions Animal::Predator, 

Animal::Prey, and Habitat. Animal defines an object property niche with a range 

restricted to a single Habitat instance; both Predator and Prey, which have instantiative 

subclasses but cannot be instantiated directly, inherit this property. Reciprocal to niche is the 

inverse Habitat property contains that is restricted to an arbitrary number of Animal

individuals (i.e., Animal.niche(Habitat)  Habitat.contains(Animal)). If 

individual Tiger is instantiated with niche = Jungle, then Tiger is automatically defined as 

a value of Jungle property contains. Additionally, Prey defines dangerous as an object 

property with range restricted to the specific Predator subclasses which pose threats, and 

Predator defines food as an object property with range restricted to the edible Prey

subclasses. 

Suppose that a number of actual habitats are surveyed for animals, and that these animals are 

entered into the ontology described above. During ontology population, the surveyors instantiate 

Habitat individuals for each habitat they sampled but do not enter values for the 

Habitat.contains property directly. Instead, the surveyors define values of Animal.niche

for all predators and prey, resulting in the automatic assignment of the animals’ instances to 

corresponding values of Habitat.contains. 

Consider a knowledge discovery application that is tasked with determining the surveyed 

habitat’s overall health. Criteria for health are based on the ratio of predators to prey in a given 

habitat, with consideration for the specific prey and predator restrictions of predator and prey 

values, respectively. Overall assessment of a habitat’s health, then, depends on information 

contained in both Animal::* and Habitat individuals. While Habitat individuals don’t 

contain information about the survival requirements of specific animals, Animal::* individuals 

have no information about their habitat or the other inhabiting individuals. 

The exemplified habitat ontology demonstrates the power of ontological modeling, inherent in 

the object-oriented representations of data entities with meaningful interrelationships. Entity 

properties indicate what and how an object is being related to, and these associations can be 

implicitly extended to yield comprehensive perspectives of entire systems. 

2.4 RDF Triple Extraction 

A semantic reasoner is an engine that infers implicit relationships from asserted facts and can be 

used for extracting object relationships from individuals in an ontology. Reasoners explore all 

levels of defined and implied relationships according to the rules of an ontology language. The 

RDF is a W3C specification for modeling data which expresses subject-predicate-object 

relationships as triples (i.e., 3-tuples of the form [subject, predicate, object]); this framework was 

chosen to describe the rules extracted from the DCIPM3 OWL database. Each RDF triple is 

marked-up by XML in conformance with the RDF schema. 
9 

All triples deriving from an individual share a common subject, given by the instance name. 

Every value for each of the individual’s properties corresponds to a unique triple, where the 

property name becomes the predicate and the value becomes the object. Extending the prior 

example of WillProposition, consider the following conversion of this individual’s 

properties into triples: 

hasStartTime = 12:22:03  (WillProposition, hasStartTime, 12:22:03) 

hasStartDate = September 6, 2008  (WillProposition, hasStartDate, September 6, 2008) 

hasParticipant‡‡ = MeganCrocker  (WillProposition, hasParticipant, MeganCrocker) 

hasParticipant = WillAnderson  (WillProposition, hasParticipant, WillAnderson) 

hasTopic = MeganCrocker  (WillProposition, hasTopic, MeganCrocker) 

Given the logical class and property names, a human reader should be able to identify the 

explicit, simple meaning in each triple and then further extrapolate a composite understanding of 

WillProposition. Outlined below is a progression of logical inferencing by which a human 

might discover some implicit relationships in WillProposition: 

Thing::Event::Communication::Conversation::PhoneCall

WillProposition 

 WillProposition was a phone call (p) 

 WillProposition was a conversation (q) 

 WillProposition was a form of communication (r) 

 WillProposition was an event (s) 

MeganCrocker instance name 

 First name of the instance is “Megan,” last name is “Crocker” (t) 

WillAnderson instance name 

 First name of the instance is “Will,” last name is “Anderson” (u) 

MeganCrocker and WillAnderson participated in WillProposition ∧ p ∧ t ∧ u 

 Megan and Will participated in a phone call (v) 

WillProposition instance name 

 Will proposed (w) 

MeganCrocker was the topic ∧ t ∧ q 

 Megan was the topic of conversation (x) 

WillProposition started at 12:22:03 on Sep. 6, 2008 ∧ v ∧ w ∧ x 

 Will proposed to Megan during a phone conversation that started at 12:22:03 on Sep. 6, 2008 

 ‡‡Although hasParticipant is a single property in Protégé-OWL, for the purposes of RDF triples extraction, both values 

are treated as single values of hasParticipant, which occurs twice in this individual. Note that in normal object modeling, 

properties must be uniquely named. 
10 

The above example is simplified; in actual RDF triple extraction, information such as the fully 

qualified class path on the first line could only be used if the encoded parent-child relationships 

were explicitly broken down into triples (e.g., [Conversation, parentOf, PhoneCall] or 

[WillProposition, instanceOf, PhoneCall]). Semantic reasoners use the process exemplified by 

WillProposition to perform RDF triple extraction. 

3. Summary and Conclusions 

Within the demanding net-centric environment of information processing, the fusion of data 

extracted from various free-form message databases is crucial. The development of high-level 

data fusion products addresses this challenge; however, development relies on the availability of 

large, accurate data sets. The DCIPM3 project resulted in the creation of a notional, albeit 

realistic, detailed scenario and TOEL encompassing a multitude of social interactions. An 

extensive message set was then derived from the TOEL that includes communications between 

subjects. The detailed, realistic scenario and TOEL serve as ground truth documents. The 

resulting ontological database is suitable for use in future testing of knowledge discovery tools 

for providing a system from which to inference rules. The OWL was used to create an ontology 

populated with RDF triples from the message set. 

The creation of the ontological database—supported by ground truth scenario documents—is a 

critical step towards providing the information fusion community the needed resources to 

accurately evaluate knowledge discovery tools. 
11 

4. References 

1. Department of Defense. Net-Centric Environment. Joint Functional Concept. 7 April 2005 

http://www.dtic.mil/futurejointwarfare/ concepts/netcentric_jfc.pdf, (accessed 2 June 2008). 

2. Papazoglou, M. P. Service-Oriented Computing: Concepts, Characteristics and Directions. 

Tilburg University, INFOLAB, Dept. of Information Systems and Management, Tilburg, The 

Netherlands. Proceedings of the Fourth International Conference on Web Information 

Systems Engineering (WISE’03), 2003.

http://ieeexplore.ieee.org/iel5/8885/28063/01254461.pdf?isnumber=28063&prod=STD&arn

umber=1254461&arnumber=1254461&arSt=+3&ared=+12&arAuthor=Papazoglou%2C+M.

P (accessed 2 July 2008). 

3. Hartman, F; Reynolds, H. Was the Universal Service Registry a Dream? Dec 2, 2004. 

http://soa.sys-con.com/node/47278 (accessed 2 July 2008). 

4. Thomas, M. U.S. Army Research Laboratory: Aberdeen Proving Ground, MD. Private 

communication. 2 June 2008. 

5. Protégé home page. http://protege.stanford.edu/overview/index.html (accessed 2 July 2008). 

6. 58--Request for Information for Soft Target Exploitation and Fusion (STEF) Army 

Technology Objective. Federal Business Opportunities. U. S. Army Materiel Command, 24 

July 2008. 

https://www.fbo.gov/index?s=opportunity&mode=form&id=e7591d423a06ef5aff54cea12af1

b1a9&tab=core&_cview=0 (accessed 10 July 2008). 

7. Army Science and Technology document. http://www.fas.org/man/dod101/sys/land/wsh2007/sci.pdf (accessed 28 July 2008). 
12 

List of Symbols, Abbreviations, and Acronyms 

ARL U.S. Army Research Laboratory 

ATO Army Technology Objective 

DCIPM3 Database Creation for Information Processing Methods, Metrics, and Models 

HUMINT Human Intelligence 

IP information processing 

OWL Web Ontology Language 

RDF Resource Description Framework 

SGA Student Government Association 

SOA service-oriented-architectures 

STEF Soft Target Exploitation and Fusion 

TOEL time-ordered event list 

W3C World Wide Web Consortium 
13 

NO. OF 

COPIES ORGANIZATION 

 1 ADMNSTR 

 ELEC DEFNS TECHL INFO CTR 

 ATTN DTIC OCP 

 8725 JOHN J KINGMAN RD STE 0944 

 FT BELVOIR VA 22060-6218 

 1 DARPA 

 ATTN IXO S WELBY 

 3701 N FAIRFAX DR 

 ARLINGTON VA 22203-1714 

 1 CD OFC OF THE SECY OF DEFNS 

 ATTN ODDRE (R&AT) 

 THE PENTAGON 

 WASHINGTON DC 20301-3080 

 1 US ARMY RSRCH DEV AND ENGRG 

 CMND 

 ARMAMENT RSRCH DEV AND 

 ENGRG CTR 

 ARMAMENT ENGRG AND TECHNLGY 

 CTR 

 ATTN AMSRD AAR AEF T J MATTS 

 BLDG 305 

 ABERDEEN PROVING GROUND MD 

 21005-5001 

 1 PM TIMS, PROFILER (MMS-P) 

 AN/TMQ-52 

 ATTN B GRIFFIES 

 BUILDING 563 

 FT MONMOUTH NJ 07703 

 1 US ARMY INFO SYS ENGRG CMND 

 ATTN AMSEL IE TD F JENIA 

 FT HUACHUCA AZ 85613-5300 

 1 COMMANDER 

 US ARMY RDECOM 

 ATTN AMSRD AMR W C MCCORKLE 

 5400 FOWLER RD 

 REDSTONE ARSENAL AL 35898-5000 

 2 US ARMY RSRCH LAB 

 ATTN AMSRD ARL CI IC C SLOCUM 

 ATTN AMSRD ARL CI IC M MCVEY 

 BLDG 321 ROOM 19 

 ABERDEEN PROVING GROUND MD 

 21005 

NO. OF 

COPIES ORGANIZATION 

 4 US ARMY RSRCH LAB 

 ATTN AMSRD ARL CI IC F SMALL 

 ATTN AMSRD ARL CI IC K KING 

 ATTN AMSRD ARL CI IC S MURRAY 

 ATTN AMSRD ARL CI IC 

 W TANENBAUM 

 BLDG 321 ROOM 8 

 ABERDEEN PROVING GROUND MD 21005 

 10 US ARMY RSRCH LAB 

ATTN AMSRD ARL CI IC A BORNSTEIN 

 BLDG 321 ROOM 8 

 ABERDEEN PROVING GROUND MD 21005 

 1 US ARMY RSRCH LAB 

 ATTN AMSRD ARL CI IC D WELSH 

 BLDG 321 ROOM 1A 

 ABERDEEN PROVING GROUND MD 21005 

 1 US GOVERNMENT PRINT OFF 

 DEPOSITORY RECEIVING SECTION 

 ATTN MAIL STOP IDAD J TATE 

 732 NORTH CAPITOL ST NW 

 WASHINGTON DC 20402 

 1 US ARMY RSRCH LAB 

 ATTN AMSRD ARL CI OK TP 

 TECHL LIB T LANDFRIED 

 BLDG 4600 

 ABERDEEN PROVING GROUND MD 

 21005-5066 

 1 DIRECTOR 

 US ARMY RSRCH LAB 

 ATTN AMSRD ARL RO EV 

 W D BACH 

 PO BOX 12211 

 RESEARCH TRIANGLE PARK NC 27709 

 4 US ARMY RSRCH LAB 

 ATTN AMSRD ARL CI OK PE TECHL PUB 

 ATTN AMSRD ARL CI OK TL TECHL LIB 

 ATTN IMNE ALC HRR 

 MAIL & RECORDS MGMT 

 ATTN AMSRD ARL CI I B BROOME 

 ADELPHI MD 20783-1197 

TOTAL: 31 (1 PDF, 1 CD, 29 HCS)
14 

INTENTIONALLY LEFT BLANK.