Technologies for Army Knowledge Fusion 

by Richard Scherl and Dana L. Ulery 

ARL-TR-3279 September 2004 

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Army Research Laboratory 

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ARL-TR-3279 September 2004 

Technologies for Army Knowledge Fusion 

Richard Scherl 

Department of Computer Science 

Monmouth University 

Dana L. Ulery 

Computational and Information Sciences Directorate, ARL 

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14. ABSTRACT 

The fusing of information to provide a real-time situational awareness and common relevant operational picture of events both 

within and outside the battlespace requires the integration and use of heterogeneous civilian and military sources. Knowledge 

fusion, also called information fusion and multisensor data fusion, names the body of techniques needed to accomplish this 

critical need of the U.S. Army. The area builds upon and integrates results from a variety of fields, including multiagent 

systems, information integration, the semantic web, and the classical (mathematically based, engineering in orientation) fusion

methodologies. The goal of work in knowledge fusion is to intelligently fuse massive amounts of heterogeneous information 

(not only from sensors, but also from databases, files, web pages, etc.) into a form which may be used by humans to yield 

actionable knowledge. This report surveys the literature relevant to the evolving field of knowledge fusion. 

15. SUBJECT TERMS

knowledge fusion, multisensor data fusion, information integration, semantic web, intelligent agents 

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Contents 

1. Introduction 1

1.1 Motivation .......................................................................................................................1 

1.2 Discussion of Terminology .............................................................................................2 

1.3 Outline of Report.............................................................................................................3 

2. Background 4

2.1 Battlespace.......................................................................................................................4 

2.2 Sustaining Base ...............................................................................................................7 

2.3 Homeland Security..........................................................................................................8 

2.4 Some Issues .....................................................................................................................8 

3. Motivating Examples 9

3.1 Movement Analysis Battlespace Challenge Problem .....................................................9 

3.2 Workaround Battlespace Challenge Problem................................................................10 

3.3 Joint Warrior Interoperability Demonstration 2003......................................................10 

3.4 Homeland Security Problem .........................................................................................11 

4. Technologies 11

4.1 Data Fusion....................................................................................................................12 

4.2 Information Integration .................................................................................................14 

4.3 Ontologies and the Semantic Web ................................................................................17 

4.3.1 Ontologies ...........................................................................................................17 

4.3.2 Knowledge Representation Systems/Languages.................................................18 

4.3.3 Markup Languages..............................................................................................18 

4.3.4 Semantic Web......................................................................................................19 

4.3.5 Intelligent Search and Retrieval ..........................................................................19 

4.3.6 Web-Site Management........................................................................................19 

4.4 Advanced Representation and Reasoning .....................................................................20 

4.4.1 Plan Representation.............................................................................................20 

4.4.2 Plan Recognition .................................................................................................20 

4.4.3 Abduction and Belief Revision ...........................................................................21 

4.4.4 Logic Programming.............................................................................................21 
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4.4.5 Uncertain Reasoning ...........................................................................................21 

4.4.6 Real-Time Problem Solving................................................................................22 

4.5 Contemporary Database Topics ....................................................................................22 

4.5.1 Querying Streams................................................................................................23 

4.5.2 Querying Unstructured Information Sources ......................................................23 

4.5.3 Web Mining.........................................................................................................23 

4.5.4 Data Mining.........................................................................................................24 

4.5.5 Information Retrieval ..........................................................................................24 

4.6 Architectures .................................................................................................................24 

4.6.1 Agents..................................................................................................................25 

4.6.2 Grids ....................................................................................................................25 

4.6.3 Web Services.......................................................................................................25 

4.6.4 Distributed Architectures ....................................................................................26 

5. Analysis and Summary 26

6. References 29

Distribution List 48 
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1. Introduction 

1.1 Motivation 

The U.S. Army has identified a critical need to fuse information horizontally across multiple peer 

units and vertically up and down the chain of command to create real-time and near-real-time 

knowledge at all levels of U.S. Army operations. The concept of fusing information from a wide 

variety of battlefield assets has been evolving for the last 20 years to improve command, control, 

communications, computers, intelligence, surveillance, and reconnaissance (C4ISR) capabilities 

for tactical wartime and contingency operations, situational assessment and awareness, and 

real-time collaboration. Most recently, the science and technology (S&T) initiatives identified to 

enhance the U.S. Army’s ability to accomplish its emerging Homeland Security mission has 

underscored the need to fuse multisource information to provide a Common Relevant 

Operational Picture built on shared data. 

We use the term “knowledge fusion” to emphasize the real-time integration and use of 

information from heterogeneous civilian and military sources to assist the human process of 

knowledge creation. This concept builds on the emerging field of multisensor data fusion 

technology. Multisensor data fusion focuses on the integration of data from multiple sensors to 

achieve more accuracy than could be achieved by using a single, independent sensor (Hall and 

Llinas, 2001). The Joint Directors of Laboratories (JDL) Data Fusion Working Group was 

established in 1986 to improve communications among researchers and developers. The 

two-layer, hierarchical JDL process model that resulted from its efforts identifies the processes, 

functions, and techniques applicable to data fusion. The 1998 model revision broadly defines 

data fusion as “the process of combining data or information to estimate or predict entity states” 

(Steinberg et al., 1998). Excellent overviews of multisensor data fusion are provided by 

Hall and Llinas (2001), Waltz and Llinas (1990), and Antony (1995). 

Knowledge fusion also builds on the emerging fields of multiagent systems (Subrahmanian, 

et al., 2000; Genesereth and Fikes, 1992), information integration (Wiederhold, 1992, 2002), 

(Jhingran et al., 2002); and the semantic web (Fensel, 2001; Fensel et al., 2003a). Knowledge 

fusion focuses on the integration, system techniques, and principles that increase the value of 

information produced by integrating information from multiple real-time and non-real-time 

heterogeneous sources that include, but are not limited to, sensors. 

In the battlespace, information is continuously arriving from sensors of various types on different 

platforms. The output of these sensors along with verbal reports, electronic mail and reports, 

background knowledge, current breaking web-based news reports, and other types of information 

need to be fused into a coherent picture of the immediate situation so that the best courses of 

action may be determined. In practice, the terms “knowledge fusion” and “multisensor fusion” 
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are both commonly used to refer to advanced technologies for integrating data from multiple 

sources for rapid and effective decision making. However, areas of focus and techniques for 

solving the problem tend to stem from different traditional and emerging disciplines. 

Knowledge fusion information sources may store data in various formats within databases, files, 

or on web pages. Alternatively, the data may be the real-time streaming output of various 

devices. Furthermore, the structuring or categorization of the information may be radically 

different in the various sources. Additionally, the time varying nature of different types of 

information may be different. Some data are relatively static while other sources are 

continuously changing. The goal of work in knowledge fusion is to develop techniques to 

intelligently fuse and process massive amounts of heterogeneous information from a wide range 

of distributed sources so that it may be used for decision making and problem solving. 

Knowledge is emerging as the ultimate in force multipliers. As information technology advances 

in capabilities and security, the symbiosis between machine and human increases in an 

unprecedented way. A core capability in the concept of knowledge fusion is support of human 

knowledge processes, accomplished through simplification and filtering techniques, and 

multimedia presentation techniques that can be tailored to the preferences and skills of the 

human receiver. Technologies to support these capabilities in military domains differ from those 

in civilian domains because systems on the battlefield and in recovery and consequence 

management (R&CM) situations must accommodate a real-time, on-the-move, dynamic, and 

extremely high-stress environment with common operations performed by humans with a wide 

range of skills and tasks.

1.2 Discussion of Terminology 

The words “data,” “information,” and “knowledge” are used in varying and overlapping ways 

in not only everyday discourse, but also in the scientific literature. There is the rapidly evolving 

area of data fusion which is primarily concerned with mathematical techniques to combine the 

output of sensors. The most mature area of multisensor data fusion is aimed at combining sensor 

data to determine the position, velocity, attributes, and identity of individual objects to identify 

and maneuver targets. Within the computer science field of databases, the well-established area 

of data and database integration addresses combining data sources. This field studies techniques 

to combine information from databases with different schemas, but also has been extended to 

incorporate information from less-structured sources such as files and web pages. The area of 

knowledge fusion includes both of these areas, but also defines and addresses processes that 

increase the value of information achieved by combining and relating information from a wide 

variety of sources. We do not expect to combine materialized data sources, but rather to bring 

together only selected results derived from these sources. We focus on the human problem of 

data overload caused by the current explosion of battlespace data from local and remote sensors, 

human voice and email input, web-based data, and multimedia news data. The knowledge fusion 
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problem is finding and putting together pertinent information relevant enough to make informed 

decisions and take swift action when confronted with a mountain of data. 

There is some philosophically oriented literature on the relationship between the notions of data, 

information, and knowledge (Dretske, 1999; Devlin, 1991). Although, it is not our intention to 

prescribe the way these terms should be used, a mention of this discussion does lend some clarity 

to the varying approaches to be discussed in this report, particularly since the philosophical 

discussion does at least, in a very loose sense, correspond to the way these terms are generally 

used. In ordinary usage, there is certainly a continuum from data to information and then to 

knowledge. The notion of data is the most primitive (e.g., raw data). The notion of information 

implies more interpretation and processing. Finally, knowledge carries the implication of 

general principles or facts of a propositional form, which are known to be true. 

Devlin (1999), drawing upon the work of Dretske (1999), situation semantics (Devlin, 1991), 

and also business researchers such as Davenport and Prusak (1998) argues for the utility of 

distinguishing between the terms in the following manner. Information is data along with 

meaning. And knowledge is information which has been internalized and associated with the 

ability to use the information. Knowledge is then an individual notion as it exists within a 

person. Similar views are expressed by Brown and Duguid (2000). 

The approach of situation semantics aims to establish a Mathematics of Information 

(Devlin 1991, 1999; Barwise, 1989) by further refining the view of information as data along 

with meaning. The core idea is that information is made up of some form of representation and a 

procedure for encoding and decoding the information. The encoding/decoding procedure is 

based upon constraints between the representation and the information conveyed about the 

world. An important outcome of this approach is the idea that information can only be 

understood or decoded if contextual situations are identified (Devlin, 1999). The issue of 

representing and maintaining the context (e.g., temporal, geographical, terminological, etc.) in 

which data/information are gathered, and utilizing that contextual information to combine 

various sources into a unified picture, is one that in various forms underlies much of the work 

discussed in this report. 

1.3 Outline of Report 

This report identifies and analyzes technologies relevant to U.S. Army knowledge fusion from a 

variety of fields such as multisensor data fusion, intelligent agents, semantic web, information 

integration, databases, artificial intelligence, systems engineering, and others. The goal is to 

identify and evaluate techniques that have high-potential applicability to Army knowledge fusion 

problems. 

In section 2, we describe the U.S. Army’s Future Force, the vision of the army of the future. The 

aim of knowledge fusion is to integrate critical information to enable knowledgeable action 

required by the Army’s Future Force. Section 3 describes some examples of Army knowledge 
4

fusion issues. Technologies relevant to knowledge fusion are identified and discussed in 

section 4. Finally, technologies of importance to the Army knowledge fusion problem are 

analyzed and summarized in section 5. 

2. Background 

The U.S. Army’s Future Force (Department of the Army, 2004; Williams, 2003; U.S. Army 

Training and Doctrine Command, 2002), the army of the 21st century, is being designed to be 

joint, interagency, and multinational (JIM). Land power plays a critical role in dominating the 

highly complex land environment that comprises the heart of most joint operations. There is an 

enduring and unavoidable challenge to control terrain, people, and resources on land, where 

political authority resides. The Future Force is to be an instrument that is precise, maneuverable, 

and flexible. It must be able to fight on a battlefield that is multidimensional, dispersed, 

continuous and noncontiguous in nature. 

The units to be deployed must be tailored to the operation at hand. The units will incorporate the 

appropriate equipment and personnel for the operation drawn from multiple U.S. agencies as 

well as those of other countries. The operation of the units are network-centric in that 

commanders in different places can obtain relevant information. 

A crucial role is to be played by the distributed information environment called the Global 

Information Grid (GIG), which is to provide links from the “factory to foxhole” and “space to 

mud.” The architecture of GIG needs to be designed to create a knowledge-based force in which 

soldiers receive and send the right information at the right place and time. This information 

environment is to facilitate the transformation of data into knowledge. 

In this section, the demands of the battlespace, sustaining the base, and also homeland security 

are discussed in the context of the Army’s Future Force and GIG. 

2.1 Battlespace 

The Army’s Future Force, supported by GIG, will carry out Network Centric Warfare (NCW) 

which is defined as 

an information superiority-enabled concept of operations that generates 

increased combat power by networking sensors, decision makers, and shooters 

to achieve shared awareness, increased speed of command, a higher tempo of 

operations, greater lethality, increased survivability, and a degree of 

self-synchronization (Alberts et al., 2002). 

Through the linking of knowledgeable entities in the battlespace, NCW is to convert information 

superiority into combat superiority. The backbone of this information system is an integrated 
5

communications system. This includes both wireless and wired networking. The network 

connects all battlespace entities—sensors, actors, and decision makers. 

In the network-centric model, sensors and actors are decoupled, unlike the traditional 

platform-centric model. Sensors from one platform may provide information that enters the 

network and is ultimately used by a variety of other actors and decision makers. Additionally, 

new types of sensors are available and need to be effectively used. 

Sensors designed to sense new things and maneuver in close to make distinctions 

among things we cannot now distinguish—The net result of all of these changes 

will be the proliferation of lower cost, independent sensors and actors that will 

contribute to and depend more upon distributed rather than embedded 

intelligence (Alberts et al., 2002). 

Information collected by the sensors needs to be put in a form so that it can be used by other 

battlespace entities. The other entities need to fuse the information, interpret it in context and 

understand the implications (Alberts et al., 2002). Note that the knowledge/information fusion 

issue arises immediately here because sensor information from a sensor designed for use by a 

particular battlespace entity is being made available for wider use. Additionally, we also see the 

importance of movement or positioning for the purpose of information gathering. 

The ultimate goal of this fusing is the construction of common relevant operational picture 

(CROP) (U.S. Joint Forces Command, 2004; Falvo, 2004). The CROP will provide a 

presentation of timely, fused, accurate, and relevant information that can be tailored to meet the 

requirements of the Joint force commander and the Joint force, and is common to every 

organization and individual involved in a joint operation. A CROP facilitates collaborative 

planning and assists all echelons to achieve situational awareness. 

Providing battlespace awareness to war fighters across the Joint force with 

requisite accuracy and timeliness requires that data and information from multiple 

sources be collected, processed (analyzed when necessary), transported, fused, 

placed in appropriate contexts, and presented in ways that facilitate rapid and 

accurate inferences. It also requires that actors and decision entities be provided 

by training with internal models and or decision aids or models. With this insight, 

we can observe that it requires both battlespace awareness and these cognitive 

models to generate battlespace knowledge which is in and of itself, an emergent 

network-centric property (Alberts et al., 2002). 

The integrated information found in the CROP includes (Alberts et al., 2002) the following: 

1. The location of the various friendly forces, enemy sources, and other entities. 

2. The available courses of action for both friendly forces and enemy forces. 

3. The predicted future actions and positions for all forces. 
6

4. The status and characteristics of various units. 

5. The environment, including the terrain and weather conditions. 

Battlespace awareness results from the fusion of key types of information that describe the 

battlespace. These include positions of forces and geography. But battlespace knowledge 

consists of tacit information that requires interpretations. Examples are intent and capabilities, 

and tactics of the various forces. The information used to build the CROP will come from a 

variety of sources. It will not only include the battlespace sensors but also, for example, the 

information available on news networks such as CNN. 

Yet another aspect of NCW is to capitalize upon the fact that the actor entities are 

knowledgeable. Command and control (C2) may be replaced by command and coordination. 

Thus the desired results may be achieved without the issuance of top-down detailed instructions. 

That is to say that organizational behavior could be consciously designed to be an 

emergent property that derives from the commander’s intent, as internalized by 

actor entities, the degree of battlespace knowledge available and the ability of 

decision entities to minimize the constraints imposed on actor entities by virtue of 

the resources allocated to actor entities. It is hard to overestimate the impact that 

this new dimension of command and control will have on the way we will 

approach operations in the future (Alberts et al., 2002). 

How to facilitate and control the execution of such a loose and distributed plan or task is an open 

question. 

Additionally, it is expected that use will be made of battlespace agents, which perform selected 

tasks as delegated by the decision and actor entities. We not only have links between sensors, 

actors, and decision entities, but battlespace agents also play a role. These agents are automated 

decision or information processes (Alberts et al., 2002). 

Some of the tasks that may be given to battlespace agents are as follows: 

1. Request additional information that is required by the situation. 

2. Task the sensors. 

3. Notify the decision entities that something requires immediate attention. 

4. Translate a commander’s intent into instructions. 

5. Resolve inconsistencies within the CROP. 

In summary, the vision of the future U.S. Army reveals the crucial importance of knowledge and 

information fusion in the information environment needed to support the Joint operational 

activities. Massive amounts of continuously arriving information from a variety of sensors and 

information sources, in a variety of formats and from various locations and perspectives, must be 
7

combined into a unified picture that is able to discern and present only that relevant set of 

information that the warrior needs to make informed decisions and take appropriate action. The 

fact that the CROP is available to a variety of battlespace agents produces the potential for a new 

form of C2 through coordination rather than exclusively and centrally directed control. 

Automated agents and robots are to be well integrated into this networked system in a way that 

results in a symbiotic relationship between warrior and machine. 

2.2 Sustaining Base 

Some issues found in the commercial sector also arise in the Army’s sustaining base domain, but 

with significantly different constraints and requirements due to the fact that the sustaining base 

supports a rapidly changing battlesphere with high human risk factors. 

Inventory is stored at various locations, and as it is used, orders need to be made to replenish the 

supply. Information on the nature of the items stored in various locations is kept in different 

formats on various types of databases. While as-needed work is being carried out, information 

systems need to ensure that all the required steps (e.g., maintenance operations) are performed. 

One major difference between the military case and the commercial sector is the interaction with 

the battlespace. For example, equipment must be stored at appropriate locations in anticipation 

of events in the battlespace, and events in the battlespace will initiate supply efforts on the 

sustaining base. The events of the battlespace are more difficult to predict than those that occur 

in the private sector. It may, for example, be necessary to move large numbers of soldiers from 

one location to another with very short notice and have all the necessary arrangements 

(e.g., food, housing, equipment, transportation) made. 

Furthermore, the fact that the Army’s Future Force is to draw equipment and personnel for 

operations from multiple U.S. agencies as well as those from other countries means that the 

information integration task will be immense. It is to be expected that the terminology and 

categories used to describe available equipment and personnel will be radically different between 

the Army and the various U.S. and foreign agencies with which efforts need to be coordinated. 

Finally, delay and error can result in loss of life. Information concerning the status of sustaining 

base operations can assist the enemy, with potential negative impact of the operation that again 

includes resulting loss of life. 

In summary, some sustaining base knowledge fusion tasks have similarities with those found in 

more traditional information integration tasks that arise in the commercial sector. But the 

military domain augments the challenges of these fusion tasks considerably and adds other 

critical dimensions because of the rapidly changing nature of the battlespace, the high need for 

security, and the significant impact of delay and error. 
8

2.3 Homeland Security 

As is now well known, prior to 9/11/01, there was information available which could have 

possibly alerted us to the impending attacks. The question arises as to whether an advanced 

information system could have put all the pieces together, predicted the attack, and allowed 

countermeasures to be taken. 

There is an immense amount of data available; some in public sources and other bodies of data 

explicitly gathered by different governmental agencies. The number of possible types of threats 

is immense. Additionally, these can occur at many different locations and at any time. Issues of 

knowledge and information fusion arise in putting the different pieces together to get a unified 

picture of where the likely threats are. 

Again, one sees the traditional information integration issue here. But the amount of data is vast 

and continuously changing. There is really a knowledge fusion problem beyond that of 

information integration. The question is how can the crucial pieces of information be identified 

(from among the vast amounts of unimportant information) and put together so that action can be 

taken. 

2.4 Some Issues 

Certainly, traditional information integration issues concerning how to combine information 

from heterogeneous structured (e.g., databases) and unstructured (e.g., files and web pages) 

information sources is of importance to addressing the knowledge fusion issues that are to arise 

in the U.S. Army of the future. Given the nature of the Army’s Future Force, i.e., the utilization 

of carefully tailored units with membership drawn not only from the U.S. Army, but also from 

foreign military forces as well as various U.S. Governmental agencies, there will be a 

particularly massive information integration problem. 

Given the nature of NCW, the amount of data arriving from all available sensors will be massive 

and continuously arriving. The techniques of data fusion are certainly needed. Given the 

number of sensors available, the issue of how can one automatically determine when a change in 

the data (continuously streaming data) is significant. Additionally, in NCW, the sensors used by 

a particular actor are not at the same position as the actor. The information from the various 

sensors need to be combined to give the knowledge/information needed by the actor to carry out 

its task(s) and achieve its goals. Note that sensors may be moved to particular locations 

(independently of the actors) in order to obtain information that would satisfy particular 

knowledge goals. These knowledge goals are necessary to satisfy the overall battlespace goals. 

It is necessary to represent knowledge goals and develop techniques for obtaining plans to 

achieve the knowledge goal. 

There is an added issue of the representation of plans. When a crucial piece of information 

arrives, indicating that a particular event has occurred, how is it possible to identify the plans or 

ongoing activities that are affected by this event? What changes then need to be made? 
9

Additionally, it is expected that knowledgeable actors can self-synchronize rather than being 

directed from the top. How is it possible to represent the ongoing plan or activity in such a way 

that this self-synchronization is possible? 

There may be a wide variety of automated tools or agents available on the network. Somehow, 

these need to be categorized and set up in such a way that the particular services that they 

provide can be determined either automatically or by a human user. Techniques for doing so 

need to be identified and/or developed. 

A number of areas of computer science that are certainly relevant to the Army’s Future Force 

and NCW have not been included. These are as follows: 

1. Issues related to human-computer interfaces and human factors relevant for the design of 

CROP. 

2. Computer/network security issues. 

These issues are certainly important, but the literature related to these issues is vast and outside 

the scope of this report. 

3. Motivating Examples 

It is useful to have some detailed examples of knowledge fusion issues that might arise in the 

operations of the U.S. Army to facilitate work on this topic. This section describes several 

places in the literature where the beginnings of such scenarios are found. 

3.1 Movement Analysis Battlespace Challenge Problem 

The Defense Advanced Research Projects Agency (DARPA) High Performance Knowledge 

Bases (HPKB) Project used a number of problem types and specific scenarios 

(Cohen et al., 1998). One of these was the Movement Analysis Battlespace Challenge Problem. 

The goal of this problem was to understand sensor data pertaining to the movement of vehicles. 

The sensor data were an idealized version of that produced by airborne Joint Surveillance Target 

Attack Radar System (JSTARS). This was supplemented with some intelligence reports 

concerning when enemy radar has been turned on, information on enemy communications and 

human intelligence. Information on the geography, roads, and the composition of enemy forces 

in the region was also given. 

The task of the movement analysis involved the following: 

1. Distinguish military from nonmilitary traffic. 
10

2. Identify the sites between which the military traffic is moving and determine their types 

(e.g., battle position, support area, artillery site) and significance. 

3. Identify which units are participating in each movement (convoy). 

4. Determine the purpose of the convoy (e.g., reconnaissance, engage in battle, etc.). 

5. Determine the exact types of the vehicles. 

See Cohen et al. (1998) for more details. 

Solutions to this type of scenario would directly serve the needs of the Army’s Future Force. 

The construction of the CROP in network centric warfare calls for this kind of interpretation of 

enemy actions. 

3.2 Workaround Battlespace Challenge Problem 

The purpose of this problem, also from DARPA HPKB Project, was to devise a system to help 

determine targets for destruction by determining how rapidly the enemy can bypass 

(workaround) the damage to the target (Cohen et al., 1998). Although, the researchers in the 

HPKB project had primarily air strikes in mind, the same sort of example can be used to reason 

about the choice of targets for destruction or capture by ground forces. 

Input to the task includes the following: 

1. Description of the target, the damage to it, and key features of the geography of the area. 

2. The purpose of destroying the target, e.g., to prevent a particular enemy unit from moving 

to a particular position. Additionally, the input includes the time period during which the 

enemy capability needs to be interdicted. 

3. Description of the enemy resources. 

The output of the workaround generator produces a reconstitution schedule, giving the capacity 

of the enemy to either repair the target or to find another way (workaround) of accomplishing the 

activity mentioned in point 2. The workaround generator needed substantial information about 

the engineering capabilities of the enemy. Again, solutions to this type of scenario are of direct 

relevance to the problem of supporting the Army’s Future Force. Kingston (2001) contrasts 

several approaches to this problem. 

3.3 Joint Warrior Interoperability Demonstration 2003 

Another example is the Joint Warrior Interoperability Demonstration 2003 scenario (Office of 

the Army, 2004). The scenario is set in an area of operations deep in the Pacific Ocean, centered 

on a fictitious island called Tindoro. Tindoro is divided due to a turbulent political history. The 

North, the opposition force, has created a province that is politically and economically attached 

to the mainland. The South is an independent nation with friendly relations with a nation called 
11

Rabenneste. It controls the Fingal Enclave located within the North territory. As the action 

begins, the region has been destabilized to the point of needing United Nations (UN) 

intervention. A conflict occurs for a period of 220 days. The UN inserts substantial force into 

the South, and captures Flinders Island. The North attacks Fingal Enclave. The UN 

Multinational Task Force (MTF) recaptures the Enclave. Peace Keeping Operation forces enter 

the South and provide humanitarian aid. MTF consolidates forces in the South and enforces 

peace keeping agreements. This scenario demands the ability for 43 coalition partners in 

wide-spread, multiple geographical locations to share relevant operational information reliably 

and quickly. These nations are not necessarily linked by treaty. MTF staff officers must have 

secure and reliable access to near real-time situational awareness. 

3.4 Homeland Security Problem 

A good example for homeland security is exactly what happened prior to 9/11/01. The recently 

released Congressional Report contains much material. The question is what type of information 

system would have put things together and issued warnings. The data would include the Phoenix 

Report from July of 2001, a memo from an FBI agent in the Phoenix field office indicating that 

attention should be paid to individuals from the middle east who have enrolled in U.S. flight 

schools. Additional information would be money transfers such as that from Ramzi Bin al-Shibh 

(who is assumed to be a member of a German al Qaeda cell) to Zacarias Moussaoui who was 

enrolled in a flight school in Minnesota. This type of data and the various sources of 

information, where it was available, could be developed into a testbed problem for knowledge 

fusion efforts. 

4. Technologies 

Technologies relevant to constructing the information system needed to support Army 

knowledge fusion are quite broad, covering many areas from multiple disciplines, such as 

Sensors, Databases, Artificial Intelligence, Systems Engineering, etc. We have divided up the 

literature into manageable units. To some extent, the determination of units is arbitrary and 

others may prefer a different division. But nevertheless, we feel that the categorization chosen 

here helps in understanding the wide variety of techniques which are available to address the 

knowledge fusion problem. Sections 4.1 and 4.2 contain the relatively well-established areas of 

Data Fusion and Information Integration. The two areas of Ontologies and also the Semantic 

Web are grouped together in section 4.3. Section 4.4 contains the somewhat disparate set of 

topics grouped together under the label Advanced Representation and Reasoning. These include 

planning, plan recognition, logic programming, Bayesian networks, and real-time problem 

solving. A number of topics related to databases did not properly fit into any of the earlier 

sections. These include research on querying streams, querying unstructured information 

sources, web mining, and data mining. They are in section 4.5, labeled Contemporary Database 
12

Topics. Finally, in section 4.6, topics related to the design of the overall architecture of the 

information system to support the Army’s Future Force are covered. 

4.1 Data Fusion 

Data fusion is a name for work carried out primarily with military applications in mind. The 

typical problems are object identification, combining information from multiple sensors, object 

tracking, threat assessment, and so on. The methods utilized by work in this area are generally 

numerical and mathematical techniques based on linear algebra, probability theory, stochastic 

processes, and statistics. The overall problem has been defined as follows: 

Locate and identify an unknown number of unknown objects of many different 

types on the basis of different kinds of evidence. This evidence is collected on an 

ongoing basis by many possibly re-allocatable sensors having varying 

capabilities. Analyze the results in such a way as to supply local and over-all 

assessments of the significance of a scenario and to determine proper responses 

based on those assessments (Goodman et al., 1997). 

As noted by Goodman et al. (1997), this definition could apply to the concerns of other areas 

such as pattern recognition and related aspects of artificial intelligence, e.g., those described in 

Duda et al. (2001). 

In the typical military setting, there are various platforms with sensors of different types. From 

such information, the data fusion system needs to produce reasonable hypotheses concerning the 

actual truth on the ground. How many vehicles are there? What are their positions? What are 

their velocities and directions of movements? What are their identities and military status 

(enemy, allied, or neutral)? The amount of data is vast and continuous. Various mathematical 

techniques have been developed to deal with this problem. 

These techniques include the Kalman filter, Multi-Hypothesis Estimation (MHE) filter, Joint 

Probabilistic Data Association (JPDA) filter, the Mori, Chong, Tse, and Wishner (MCTW) filter, 

Point-Process Filter, Hidden Markov Model (HMM) Filters and many others as well. 

Additionally, the techniques include the use of Belief Functions (Dempster-Shafer Theory of 

Evidence), random sets, event algebras, neural networks, and also Bayesian techniques 

(Robert, 2001; Gelman et al., 2000). Simulation and optimization techniques are also used, 

i.e., to determine sensor placement (Brown and Schaumburg, 2001). The literature is vast and a 

detailed comparison of the various techniques cannot be given here (see Hall, 1992; Waltz and 

Llinas, 1990; Sadjadi, 1996; Bar-Shalom and Fortmann, 1998; Klein, 1993). 

Some recent efforts towards improving these techniques by adding world/domain knowledge are 

due to Donald Brown and his collaborators (Power and Brown, 2002; Sobiesk et al., 1998; 

Barker et al., 1998; Bovey and Brown, 2000). The types of knowledge that they incorporate 

include knowledge of the environment (e.g., terrain) and also characteristics of the entities 

operating in the environment (e.g., membership in a group such as a convoy). Improved 
13

performance results from incorporating this additional information. This work is significant in 

that it forms a bridge between the traditional data fusion activities and the higher-level 

techniques to be discussed next. 

Donald Brown and his collaborators have also used the techniques of data fusion to predict 

criminal incidents on the basis of past occurrences and to determine which crimes are likely to 

have been committed by the same individuals (Brown, 1998; Liu and Brown, 1998; Brown and 

Hagen, 1999; Brown and Oxford, 2001; Xue and Brown, 2003). Essential to this work is a 

mathematical measure of the similarity of incidents. Clearly the same techniques can be applied 

to the prediction of incidents such as terrorist attacks or military strikes. 

A general model of data fusion, the Joint Directors of Labs (JDL) model, is laid out by White 

(1988) and then revised by Steinberg et al. (1998). The later paper defines data fusion very 

generally as the process of combining data to refine state estimates and predictions. A number of 

levels of data fusion are proposed by Steinberg et al. (1998) to categorize the different types of 

problems addressed in data fusion work. The levels are as follows: 

 Level 0: Sub-Object Data Assessment, estimation and prediction of signal/object 

observable states on the basis of pixel/signal level data association and characterization. 

 Level 1: Object Assessment, estimation and prediction of entity states on the basis of 

observation-to-track association, continuous state estimation, and discrete state estimation. 

 Level 2: Situation Assessment, estimation and prediction of relations among entities. 

 Level 3: Impact Assessment, estimation and prediction of effects on situations of planned 

or estimated/predicted actions by participants. 

 Level 4: Process Refinement, adaptive data acquisition and processing to support mission 

objectives. 

Level 0 involves things like detecting signals and finding features in an image. Level 1 involves 

the identification of physical objects, possibly by drawing upon other fusion processes, i.e., 

tracks. Groupings of these objects are postulated under level 2. Level 3 aggregates or relates the 

groupings of entities from level 2 into plans and determines how they might effect the plans of 

other parties. Finally, level 4 determines what actions should be taken perhaps as part of a new 

plan. Steinberg et al. (1998) state clearly that the information flow may go in multiple 

directions, not just from level 1 to level 2 to level 3 and then to level 4. Additionally, they point 

out that particular applications may call for a different partition of functions. 

Clearly, the JDL architecture is motivated by the integration of sensor data. It may not be 

immediately suitable for the integration of other sorts of information such as the contents of 

databases. Additionally, the higher up one goes in levels (from 2 to 3, and certainly to 4); the 

less applicable are the traditional mathematical methods of Data Fusion. Yet the overall goals 
14

are not very different from that of Knowledge Fusion. The model seems to call for or need the 

techniques that will be discussed in the remainder of this report. 

4.2 Information Integration 

The goal of work in information/data integration is to provide uniform access to multiple sources 

of information. The idea is that the user should only have to focus on specifying what is wanted 

and to let the system worry about how to obtain the answers (Levy, 1998, 1999; Levy and 

Weld, 2000). Although initial work in this area considered only databases, the work was quickly 

generalized to include web sources as well. 

Information/data integration work has been primarily carried out by database researchers. An 

introductory presentation of issues of information integration may be found in Chapter 20 of 

Garcia-Molina et al. (2002). Surveys of earlier work in this area include Batini et al. (1986) and 

the more recent work of Hull (1997). Wiederhold (1992) introduced the important notion of 

mediators, i.e., modules that mediate between the users’ workstations and data sources, using 

knowledge to transform data into information. 

The literature of the past decade describes a number of projects in this area: 

1. TSIMMIS (Garcia-Molina et al., 1997) is an information integration architecture based on 

mediators. The architecture includes the following: 

a. The OEM data model, an object oriented data model that is self describing (Nestorov 

et al., 1997; Papakonstantinou et al., 1995). It is especially suitable for semistructured 

hierarchical data such as that found on the World Wide Web. 

b. Mediators and wrappers and tools for generating them. A system for declaratively 

specifying mediators has been developed (Papakonstantinou et al., 1996). 

Papakonstantinou et al. (1995) describe a toolkit for the development of wrappers 

(software modules that extract data from a source). 

c. A query language, LOREL, especially designed to query semistructured data or data 

from several heterogeneous data sources. The queries can operate over data having 

different types and also when some of the data is absent (Abiteboul et al., 1997). 

The approach used in TSIMMIS has been described as “Global As View” (GAV)*

 because the 

mediated or global schema is described as a set of database views (i.e., queries) over the different 

information sources. The amount of computation needed to answer a query is thus minimized, 

but it is more difficult to add, delete, or modify information sources. 

 *

 Another term is query-centric. 
15

2. SIMS: The SIMS project basically takes a planning approach to the problem 

(Arens et al., 1993, 1996). Given a query, the planner considers alternative ways to find 

the data required to satisfy the query. 

3. InfoMaster: The approach used here is “Local As View” (LAV).∗ For every data source, 

it is necessary to describe the information in the source by writing a rule over the relations 

in the mediated (global) schema (Duschka and Genesereth, 1997a, 1997b). Query 

reformulation is therefore more difficult and requires a certain amount of inferencing. But 

adding additional data sources is easier. 

4. InfoQuilt: (Thacker et al., 2003) is a platform for answering complex information requests 

over data available on the web. Ontologies (see section 4.3) are used to describe 

knowledge about domains and the relationship between them. InfoQuilt uses the LAV 

approach for representing the information contained by various sources. It has a number of 

tools to facilitate the construction of the knowledge base by the user. 

5. HERMES: The HERMES system, developed by V. S. Subrahmanian and collaborators 

provides a particularly rich language for writing mediators (Subrahmanian et al., 1995; 

Subrahmanian, 1994; Adali and Subrahmanian, 1994, 1996; Lu et al., 1995, 1996; 

Adali and Emery, 1995). The language is based upon Annotated Logic Programming due 

to Kifer and Subrahmanian (1992). 

6. Information Manifold: (Levy et al., 1996a, 1996b; Kirk et al., 1995). This work is 

designed to provide an interface to structured information sources available on the internet. 

The sources seem to be generally databases. The contents of sources are described in a 

language called CARIN (Levy and Rousset, 1996, 1998) that combines Datalog and a 

description logic (see section 4.3). The system computes a query plan to answer a 

particular query given the description of the available information sources. Probabilistic 

information on the coverage of data sources has been added (Florescu et al., 1997). 

7. Nimble is a commercial system based upon XML (Draper et al., 2001) 

(see also http://www.nimble.com). 

8. Tukwila: The Tukwila information integration system is designed to handle XML data and 

in particular streaming data. As such, Tukwila can provide answers while the data are 

continuously coming (Ives et al., 1999, 2000a, 2000b, 2001, 2003). 

9. Softbot Interface to Internet: Etzioni and Weld (1994) describe a softbot interface to the 

internet which makes use of AI planning methods to satisfy the user’s queries. Etzioni 

(1996) covers general issues concerning mining the Internet. Theoretical issues pertaining 

to constructing plans for information gathering when there are costs (monetary and 

 ∗

 Another term is view-centric. 
16

temporal) on accessing different information sources are investigated by Etzioni et al. 

(1996). 

10. Ariadne (Knoblock et al., 2002a) is a system for extracting and integrating data from web 

sources. It allows users to rapidly create information agents for the web. 

The projects previously described have ranged from experimental to commercial. They have 

illustrated a variety of different techniques. But separate from particular projects such as those 

described previously, there has been quite a bit of work on general issues (both theoretical and 

practical or algorithmic) that arise in the area of information integration. These topics are 

surveyed in the following paragraphs. 

Hull (1997) and Ullman (1997) survey approaches towards the integration of information from 

multiple databases. General work on the use of description logics for information integration has 

been carried out by Lenzerini and collaborators (Calvanese et al., 1997a, 1998a, 1998c, 2002). 

They developed a description logic (along with associated inference methods) that is suitable for 

representing the various information sources as well as the global or mediated domain. The logic 

can express the relationship between concepts from the different domains. Other applications 

are data warehousing (Calvanese et al., 1998b), handling SGML documents 

(Calvanese et al., 1997b), and extensions of object-oriented modeling (Calvanese et al., 1995). 

Additional general work on the use of description basics in information integration has also been 

carried out by Beeri et al. (1997). 

General issues concerning query-answering plans for data integration systems are considered by 

Duschka, et al., (2000). In particular, they look at the notion of a maximally contained plan, a 

plan that provides all the answers that are possible to obtain from the available sources, but 

which may not necessarily be equivalent to the original query. The authors consider the cases 

when recursive plans are needed. 

Friedman et al. (1999) discuss the use of planning techniques to gather information. The user’s 

queries are translated into plans over information sources. The efficiency of information 

gathering plans and techniques for transforming plans into more efficient plans are considered in 

a number of papers (Friedman and Weld, 1997; Doan and HaLevy, 2002). Issues pertaining to 

query containment are considered by Milstein et al. (2002). Additionally, work at ISI has 

addressed the issue of planning queries in the presence of mediators that provide access to 

information distributed over heterogeneous sources (Ambite and Knoblock, 2000). 

The question of optimizing query plans with regard to cost and coverage has been considered by 

a number of researchers. Nie and Kambhampati (2001) consider cost in terms of planning cost. 

This is traded off the coverage of the plan. Kambhampati and his collaborators have also looked 

at utilizing machine-learning techniques for gathering coverage statistics (Nie and Kambhampati, 

2003; Nie et al., 2002). 
17

In a number of publications, Bertossi, Chomicki, and collaborators look at the computation of 

consistent answers from inconsistent databases (Bertossi and Chomicki, 2003; Bravo and 

Bertossi, 2003; Arenas et al., 2000, 2003; Bertossi et al., 2002). They are particularly concerned 

with databases that violate integrity constraints. The approach is to compute the minimal repairs 

that could restore consistency to the database(s) and reason over this new set of databases. 

Machine learning techniques have been proposed for learning many of the pieces of knowledge 

needed by information integration systems. Examples are learning the mapping between source 

and mediated schema (Doan et al., 2000), learning mappings between ontologies 

(Doan et al., 2002), and more generally using statistical methods for finding mappings between 

different schemas (HaLevy and Madhavan, 2003). Kushmerick (2000) analyzes a number of 

techniques for automatically inducing wrappers for web sites. Craven et al. (2000) developed a 

trainable system that extracts information from the web and produces a knowledge base. 

Cohen (2000) describes a language called WHIRL that combines both textual (concepts based on 

information retrieval [I.R.]) and logic-based representation systems (e.g., datalog) into a single 

language for information representation. The language is useful for a variety of information 

integration tasks including the generation of wrappers. 

Work in the area of information integration often makes use of the notions of mediators and 

wrappers. These are in some respect similar to the notion of agents. There are different pieces 

of software serving particular purposes, but it is a relatively passive model. The mediators and 

wrappers do not monitor their environment and take appropriate actions given their goals. For 

time varying data, it seems likely that the more active model of agents would be appropriate 

rather than the more passive model of mediators and wrappers. For keeping the COP up to date, 

sources will have to notify the COP of significant changes. How they would determine which 

changes are significant is an open question at this point. There may have to be extensive 

communication going on in both directions between the CROP and the wrappers. 

4.3 Ontologies and the Semantic Web 

This section covers several related areas. There has been an interest in ontologies, a 

machine-understandable specification of a conceptualization of a domain (Gruber, 1993), for 

some time in the area of artificial intelligence (AI)/knowledge-based systems. A number of 

languages have been developed to represent ontologies. The effort known as the semantic web 

has moved this work onto the web (using XML) so that the terminologies defined by these 

ontologies can now categorize data available on the web. 

4.3.1 Ontologies 

Overviews of recent work on ontologies have been written by Fensel (2001) and McGuinness 

(2003). For a more philosophical perspective, see Guarino (1998). Of particular recent 

importance is the convergence between web technology and ontologies. This convergence has 

resulted in efforts to create ontology languages based upon XML and RDF. The construction 
18

and manipulation of ontologies are crucial to the development of the information system to 

support NCW and the Future Force. Ontologies provide a formal specification terminology used 

to categorize objects and events. Without such ontologies, it is simply not possible to 

automatically manipulate the terminologies so defined for fusion tasks. 

4.3.2 Knowledge Representation Systems/Languages 

There are a number of knowledge representation languages that have been developed to 

represent ontologies. Some of the better known are as follows: 

1. Ontolingua is a frame-based language designed to facilitate the use of ontologies within 

multiple applications. 

2. Loom was developed at ISI and is based on a description logic or classifier system. 

3. See Reed and Lenat (2002) for a recent discussion of applications of CYC. See Lenat and 

Guha (1989) for the original description of the project. The CYC project includes both the 

development of an ontology language, a set of tools, and also a large ontology designed to 

be used in a wide variety of applications. 

4. GKB (Generic Knowledge Base) Editor (developed at SRI) is a graphical tool for 

interactive knowledge base browsing and editing (Karp et al., 1998). 

Karp (1993) has produced a (now somewhat dated) comparison of many of these systems. 

Much work in the area of ontologies is based upon frame languages. A more flexible alternative 

that automatically computes the classification of instances is description logics. The literature on 

description logics is quite large. See Calvanese et al. (1998) for an overview of their use in 

modeling. A book length overview of the entire area of description logic research is now 

available (Bader et al., 2003). 

Some work, especially at ISI, has been done on developing tools to assist in the gathering of 

knowledge (Blythe et al., 2001; Blythe, 2001; Gil and Ratnakar, 2002). Some of the projects are 

as follows: 

1. Trellis (Trellis, Information Sciences Institute, USC [2004]; Gil, 2003). 

2. Expect (Expect, Information Sciences Institute, USC [2004]). 

3. Temple (Temple, Information Sciences Institute, USC [2004]).

4.3.3 Markup Languages 

Markup languages such as HTML, XML, XML Schema, and XHTML have become ubiquitous 

today. Standards for such languages are set by the W3C organization (http://www.w3c.org). 

There are numerous books describing XML (including its extensions such as XML Schema and 

XHTML) and applications to electronic commerce (Martin et al., 2000; Schmelzer et al., 2002). 
19

4.3.4 Semantic Web 

The network-centric environment for Army knowledge fusion will be based upon the semantic 

web. Some general introductions are Berners-Lee et al. (2002) and Fensel (2003a, 2003b). RDF 

(Lassila and Swick, 1999) is a basic ontology language based upon XML. Many of the semantic 

web languages are based upon RDF. See Patel-Schneider and Fensel (2002) for a discussion of 

various issues related to the layering of languages that make up the semantic web. 

Heflin et al. (2003) discuss the web markup language SHOE (not based on RDF). McGuinness 

et al. (2003) describe the language DAML-ONT, which is the ontology portion of DAML 

(DARPA Agent Markup Language). The language OIL is described by Klein et al. (2003). 

Both OIL and the combination of DAML and OIL (DAML+OIL) are discussed by 

Fensel et al. (2003b). A more recent language being considered is OWL (Smith, 2003). All of 

these languages are based on RDF and have been considered by the W3C. OWL is currently a 

W3C candidate recommendation. 

Much work in the semantic web is devoted to the development of ontologies, ontology 

languages, and support tools for the specification of the information available on web pages and 

also for appropriately making web services available to automated agents. For discussions of 

various tools see (Omelayenko et al., 1993; Sure and Studer, 2003; Klein et al., 2003; Engels and 

Lech, 2003; Sure et al., 2003). Lassila and Adler (1993) discuss how web services (semantically 

specified) may be utilized by a hand-held (PDA-like) device. A similar device may be utilized 

by soldiers in the field to access services available on the network (GIG). 

4.3.5 Intelligent Search and Retrieval 

Maedche et al. (2003) describe techniques for semantically querying and navigating repositories 

of materials annotated with a semantic markup language. There has also been work on the 

storage and retrieval of RDF annotated data. See, for example, Davies et al. (2003) and also 

Broekstra et al. (2003). 

Service retrieval (e.g., online web services) is an important topic that is likely to play a major 

role in the information system needed to support the Army’s Future Force. One approach, due to 

Klein and Bernstein, is based upon the use of an ontology of processes (Klein and Bernstein 

2001; Bernstein and Klein, 2002). The work makes use of an approach to modeling processes 

that has associated with it a number of tools and other applications (Klein and Dellarocas, 2000; 

Bernstein et al., 1999; Klein, 2003). 

4.3.6 Web-Site Management 

A tremendous amount of information will be available throughout the GIG for inclusion into the 

CROP. Automating the process of gathering the relevant data for inclusion is a major issue. 

There has been some relevant work on web-site management. In particular two systems, 

Strudel and Tiramisu have been developed (Fernández et al., 1997a, 1997b, 2000; 

Anderson et al., 1999). These allow the site designer to specify in a declarative fashion what 

data should be included in the website and how that data should be visually presented. 
20

4.4 Advanced Representation and Reasoning 

This section covers a number of topics related to representation and reasoning that are likely to 

be essential to the success of NCW. One is the development of a flexible notion of plans. This 

has been mentioned earlier. Additionally, it is necessary to recognize the plan of enemy forces 

given the evidence available. This problem, essential to the vision of NCW, has been 

investigated under the name of plan recognition, and also it is a specific application of abductive 

reasoning. Other types of relevant reasoning are logical reasoning such as that found in the 

literature on logic programming, and also probabilistic or uncertain reasoning. Finally, real-time 

behavior is likely to be important in many cases, while these reasoning methods often do not 

exhibit real-time behavior. Work in the area of real-time problem solving addresses the issue of 

obtaining responses in a time-constrained setting. 

4.4.1 Plan Representation 

The literature on planning within AI is vast. Additionally, see Allen et al. (1991) for several 

innovative articles in this area. Additionally, Devanbu and Litman (1996) provide a description 

logic (CLASP) to represent plans. The notion of subsumption now applies between plan 

concepts and also between plan concepts and plan instances. This work may be useful in 

addressing the need for a flexible representation for plans and activities. 

GoLog (Levesque et al., 1997; Reiter, 2001) is a high-level programming language for agents 

based on the situation calculus. The language includes nondeterministic constructs and 

incorporates deduction into the planning mechanism. Again, the work may prove useful as a 

basis for a flexible plan representation language. 

4.4.2 Plan Recognition 

There is a body of literature on plan recognition and more generally on abductive reasoning. 

Here, rather than constructing a plan and executing it, the goal is to determine on the basis of the 

available evidence, what is the plan that is being carried out by perhaps the enemy forces. This 

work will enable the COP to present information on the likely intent and future positions of the 

enemy forces. 

An important early work on plan recognition is put forth by Kautz (1991). A formal theory of 

plan recognition is developed in which the problem is precisely stated. In this work, the goal of 

the plan recognition is to produce the most specific plan that accounts for only the observed 

observations. Therefore, it does not seem amenable to the incorporation of other types of 

evidence in support of the likelihood of alternative plans. 

Charniak and Goldman (1993) present a method of plan recognition in which candidate 

explanations (i.e., plans) are first retrieved and then are assembled into a Bayesian network. 

Then the observed actions are entered and Bayesian updating takes place. The result is a 

probabilistic ranking of candidate plans on the basis of their likelihood in being the actual plan. 
21

Poole (1993) presents a framework for the abduction of Horn clauses, based on the combination 

of a Bayesian network with Prolog. In Goldman et al. (1999), Poole’s work is used as the basis 

for an approach to plan recognition. These approaches seem to have the flexibility to be 

extended to allow a variety of types of evidence to affect the likelihood ranking of plans. 

Closely related to plan recognition is the abstraction of higher level concepts from temporal data. 

Shahar (1997) gives a general knowledge-based framework for such a temporal abstraction 

process. Another related topic is work on finding patterns in temporal data or the dividing of 

sequences into meaningful episodes (Oates et al., 1997; Cohen, 2001; Cohen and Adams, 2001). 

4.4.3 Abduction and Belief Revision 

Plan recognition as discussed in the previous section is closely related to the more general topic 

of abductive reasoning. The problem is to infer what must have been true to account for current 

observations. Especially relevant is abduction in the context of the occurrence of actions. Some 

relevant literature is Baral (2000), Poole (1989), and Selman and Levesque (1996). 

Josephson and Chandrasekaran (2003) have proposed an architecture for an information system 

in which abductive inference plays a major role. 

Another related area is belief revision (Gärdenfors, 1988, 1992; Goldszmidt and Pearl, 1996; 

Boutilier, 1998; Friedman and Halpern, 1997). The issue here is how to revise our beliefs given 

new information. It is often necessary to eliminate beliefs to accommodate the new information 

and there may be different possible choices that can be made with regard to which beliefs to 

eliminate. 

4.4.4 Logic Programming 

The literature on logic programming is quite large. Applications include abduction, 

nonmonotonic reasoning, planning, reasoning about actions, and various forms of problem 

solving. Baral and Gelfond (1994) survey the use of logic programming for knowledge 

representation. The recent book by Baral (2003) covers a newer logic programming language 

AnsProlog which is especially suitable for nonmonotonic reasoning. Reasoning about actions 

within logic programming languages has been a topic of major interest. Baral, Gelfond, and 

collaborators have proposed a variety of action description languages with differing capabilities 

(Baral et al., 1997). An alternative approach is the use of GoLog (Levesque et al., 1997; Reiter, 

2001), mentioned earlier. 

4.4.5 Uncertain Reasoning 

The literature on uncertain or probabilistic reasoning is quite large. Many of the techniques are 

related to those used in data fusion as discussed in section 4.1. Shafer and Pearl (1990) is a 

collection of classic papers on the topic of uncertain reasoning in general. Pearl (1998) is a 

book-length introduction to the combination of graphical models and Bayesian reasoning known 

as Bayesian Networks. The work of Cowell et al. (1999) and Jensen (1996) are excellent 
22

introductions to probabilistic expert systems. Pearl (2000) is a book devoted to exploring the 

notion of causality, but in the process lays out the latest thinking of the author on Bayesian 

networks and probabilistic reasoning in general. 

The use of machine learning techniques to learn a type of Bayesian network (Tree Augmented 

Naive Bayes) useful for classification problems is discussed by Friedman et al. (1997). Issues of 

using Bayesian networks to cluster dynamic processes are considered by Ramoni et al. (2001). 

These techniques are potentially useful in computing the most probable generating processes for 

some observed sensor data when that sensor data is continuous. Again, these techniques would 

supplement those traditionally used in data fusion. 

In a number of papers, A. Pfeffer, D. Koller, and collaborators combine Bayesian Networks with 

the types of more expressive structures found in knowledge representation languages (Koller 

et al., 1997; Pfeffer et al., 1999; Koller and Pfeffer, 1997, 1998; Pfeffer and Koller, 2000). The 

result is probabilistic versions of frame-based languages and description logics. This approach is 

designed to be suitable for representing the battlespace domain. A related approach, but with an 

added temporal component, has been proposed recently by Sanghai et al. (2003). These methods 

may turn out to be important in integrating the results of data fusion with higher level reasoning 

both of a probabilistic and a logical nature. 

4.4.6 Real-Time Problem Solving 

Because certain computations will have to provide an answer within a limited time period, 

computing in time-constrained environments is of importance. Unfortunately, many of the 

reasoning methods discussed earlier do not necessarily have good time-constrained behavior and 

therefore techniques for performing reasoning with time constraints are likely to be important in 

the development of the reasoning methods needed to support those aspects of Army knowledge 

fusion that will demand real-time behavior. 

A classic paper is by Boddy and Dean (1994) in which anytime algorithms are discussed. An 

anytime algorithm will provide an answer at any point in the computation and with more time the 

quality of the answer will only improve. The composition of real-time modules is discussed by 

Zilberstein and Russell (1966). Other aspects are considered in Hansen and Zilberstein (2001) 

and Horvitz (2001). Real-time diagnosis and the construction of plans to discriminate among 

possible hypotheses are discussed in Ash and Hayes-Roth (1996). 

4.5 Contemporary Database Topics 

A number of currently active research areas within the field of databases, broadly construed, are 

potentially quite relevant to the concerns at hand. These include the topics of querying streams 

and querying unstructured information sources. Also included here are the topics of data mining, 

web mining, and information retrieval. 
23

4.5.1 Querying Streams 

Recently, there has been interest within the field of databases in developing methods for 

querying streams of continuous data. This work is potentially useful for monitoring sensor data 

or information on credit card transactions. Much work has been carried out by J. Widom along 

with students and collaborators at Stanford University (Arasu et al., 2002, 2003; Babcock et al., 

2002; Babu and Widom, 2001). 

Querying streams of XML data is considered by a number of researchers (Ives et al., 2000b; 

Gupta and Suciu, 2003). Related issues in routing XML data to the appropriate place are 

considered by Gupta et al. (2003). See also the Tukwila system mentioned earlier. 

The ability to query streams is a critical aspect of Army knowledge fusion. Particular sensors 

may need to be continuously monitored and alarms sounded (i.e., messages sent to the 

appropriate people or agents) when significant changes occur. For homeland security, the 

querying (monitoring) of streams of credit card transactions, information from shipping 

manifests or passenger lists, also is a useful tool. 

4.5.2 Querying Unstructured Information Sources 

Developing the capability to process data that are semistructured is an important aspect of 

current database research. These are data that do not conform to a particular schema as in a 

database, but are to varying degrees self-describing. Much of the data are in XML format, but it 

could also be in HTML, or in one of the semantic web languages discussed earlier in section 4.3. 

It is to be expected that much of the data exchanged over the network supporting NCW will be 

semistructured, using XML or something similar. Additionally, data available on the web are in 

this format. The capability of querying such data is crucial. 

An excellent introduction to work in this area is the book by Abiteboul et al. (2000). A query 

language, LOREL, was developed as part of the TSIMMIS Project and is especially designed to 

query semistructured data. The queries can operate over data having different types and also 

when some of the data are absent (Abiteboul et al., 1997). This query language was migrated to 

work on XML (Goldman et al., 2000). Work on developing efficient algorithms for the 

evaluation of queries over semistructured data has been carried out by Suciu (2002). A general 

discussion of issues involved in systems developed for querying XML data may be found in 

Deutsch et al. (1999). Query languages for querying web sites are surveyed by Florescu et al. 

(1998). These are designed for HTML pages. Querying RDF and RDF Schema is discussed by 

Broekstra et al. (2003). 

4.5.3 Web Mining 

Recently there has been a growing interest in web mining; extracting useful information from 

web pages. Chakrabarti (2003) has written a relatively technical survey of the work on the topic. 

A less technical work oriented towards business applications is Linoff and Berry (2001). 
24

Knoblock et al. (2002b) apply machine learning techniques to the extraction of data from web 

pages. Most of this work is dealing with HTML/XML pages, rather than pages annotated in the 

fashion of the semantic web. If the pages are so annotated, then the mining issues are rather 

different, and one is able to obtain much more reliable information. 

4.5.4 Data Mining 

Data mining technology clearly has high relevance to Army knowledge fusion. The literature on 

data mining is large and growing rapidly. Good surveys of the available techniques in this area 

include books by Witten and Frank (1999) and Han and Kamber (2001), and an excellent article 

by Brown (2002) that discusses data mining in the context of work in the area of data fusion. 

4.5.5 Information Retrieval 

The literature on information retrieval is also vast and cannot be covered here. These techniques 

are of high importance to Army knowledge fusion because much information is available in 

textual sources. 

One relatively recent and excellent survey is the book by Baeza-Yates and Ribeiro-Neta (1999). 

Woods (1997) has been working on an interesting approach called Conceptual Indexing. This 

approach tackles directly the paraphrase problem of different words being used in the texts and 

in the query. He represents the semantic relationship among concepts, morphological structure, 

and different words. The resulting taxonomy is then used in retrieval. 

4.6 Architectures 

The Army is moving toward network-centric systems, pervasive use of sensors, and the 

introduction of robots into multiple force levels. Hardware and software architectures are 

essential for systematically structuring systems to support the fusion of these concepts and to 

achieve the vision of a tightly integrated Future Force made up of humans and machines working 

together. Large-scale distributed applications using decentralized infrastructures, distributed 

operating systems, multi-agent architectures, and dynamic adaptability will be required. In 

addition, architecture technologies for information integration that enable fusion at the 

application level are needed. Applications will need to interact with a full range of components, 

ranging from databases, application servers, and data warehouses, to search engines, agents, data 

streams, and sensors. Classical architecture research is a maturing field, but software 

architectures designed to address the full range of integration issues required to support the 

knowledge fusion required for the Future Force are only now emerging. 

Shaw (2001) has written an excellent analysis of the evolution and prospects of software 

architecture research. The JDL data fusion model (White, 1988; Steinberg et al., 1998) describes 

an architecture that involves multiple agent-like nodes communicating with each other. Software 

architecture approaches that address the engineering of large, complex software systems include 

new technologies for component composition and software connector technology for component 
25

interaction (Mehta et al., 2000). In the following sections, we discuss several related topics that 

pertain to the distributed architecture of GIG. 

4.6.1 Agents 

The literature on agents is quite large. Only a few relevant pointers can be mentioned here. 

Subrahmanian et al. (2000) give an excellent survey of much of the work in this area. The 

remainder of the book covers the IMPACT (Interactive Maryland Platform for Agents 

Collaborating Together) architecture for building an agent-based system. Bradshaw (1997) is a 

collection of articles covering various applications of agents. 

GoLog (Levesque et al., 1997; Reiter, 2001) is a high-level programming language for agents 

based on the situation calculus. The idea is that rather than perform planning, an agent can 

search for a legal execution sequence of a high-level plan/program by reasoning about the effects 

and preconditions of actions. Knowledge and knowledge-producing actions have been 

incorporated into the language (Scherl, 2003). ConGolog (De Giacomo et al., 2000) is a 

concurrent version of GoLog. It includes facilities for prioritizing the execution of concurrent 

processes, interrupts and exogenous actions. 

Lesser and co-authors (Lesser et al., 2000) describe an agent named BIG, designed to gather 

information over the World Wide Web in support of a decision process. One of its 

characteristics is the capability to reason about the resource trade-offs of different information 

gathering approaches. 

4.6.2 Grids 

Recently there has been work in an area called grid computing. The issue is how to perform 

computation in a distributed environment where different computational resources and data 

sources are located on a network (see, for example, Blythe et al., 2003a, 2003b; 

Deelman et al., 1993). AI planning techniques are used to rapidly generate plans or workflows 

for solving particular computational problems; given a user specification of the desired result. It 

is likely that similar techniques may be needed to answer queries over the network established to 

support the Future Force. 

4.6.3 Web Services 

Languages developed as part of the semantic web endeavor not only categorize information, but 

also services (Peer, 2002; Paolucci et al., 2002). Daml-S, the portion of DAML+OIL for 

describing web services, is discussed by Ankolekar et al. (2002). McIlraith and Son (2002) use 

Golog for composing web services. Tate et al. (2003) specify agents that plan to achieve goals 

by utilizing available web services. 
26

4.6.4 Distributed Architectures 

Architectures for information integration are emerging as a result of the explosion of the Internet 

to address the problem of integrating large-scale application systems that must interact with 

databases, distributed servers, workflow systems, search engines, message queues, Web 

crawlers, mining and analysis packages, and a large variety of programming interfaces such as 

ODBC, JDBC, Java objects, SQL, XML, WSDL, SOAP, etc. (Roth et al., 2002). The IRIS 

project at MIT is developing a novel decentralized infrastructure, based on distributed hash 

tables that will enable a new generation of large-scale distributed applications (Dabek et al., 

2003). 

In a distributed setting, processes/agents need to communicate with other processes/agents even 

if they do not know the location, identity, or even the presence of the others with whom they 

need to communicate. Techniques have been developed to decouple the producers of 

information and services from those who consume or use the information or services. One such 

architecture for accomplishing this decoupling is the publish/subscribe paradigm. It provides for 

mediators that enable the communication between publishers/producers and clients/subscribers. 

If a publisher/producer has information or a service, it publishes it via the mediator and the 

mediator communicates the fact that the information or service is available to those clients who 

have subscribed to receive notices of the appropriate kind of events. See Eugster et al. (2003) for 

a survey of work on the publish/subscribe paradigm. An alternate (but related) model for a 

loosely coupled distributed system based on the concepts of event generation, observation, and 

notification is discussed by Rosenblum and Wolf (1997). 

Minsky and collaborators (Minsky and Ungureanu, 2000; Murata, 2003) have developed a 

mechanism for regulating such open distributed systems. This method is called Law Governed 

Interaction (LGI) and provides a scalable method for ensuring that such a system will continue to 

maintain desired properties even as new agents are added and existing agents are deleted or fail. 

5. Analysis and Summary 

The construction of information systems that reliably support the Future Force concept depends 

upon fundamental advances in the field of knowledge fusion. The Future Force concept is not 

achievable solely using today’s technologies. These advances require that the state of the art in 

the areas identified in this report be advanced and approached from a multidisciplinary 

perspective. Specifically, it will be necessary to bring these different areas together, to combine 

the different approaches into a single information system in which different pieces of 

information are analyzed with a variety of techniques, and the results are fused together into 

knowledge of the overall picture. Systems that achieve portions of this goal today are developed 

in an ad hoc manner, making such systems unreliable and unflexible, often unable to scale-up or 
27

adapt to dynamic changes in a real-life military environment. The potential for rapidly 

introducing specific areas of new fundamental research and combining this research with 

components ready or near-ready for implementation today is strong. 

Currently, representation and reasoning have not played a large role in data fusion research. 

Going forward, it is essential that these approaches become a major part of knowledge fusion 

research in order to gain fundamental understanding of the problem and find reliable solutions. 

This report has considered a variety of languages for representing knowledge and information. 

In many cases, these languages have been provided with a formally defined syntax and 

semantics. Reasoning methods and tools have been developed for these languages. 

In particular, the following areas are well developed: 

1. Information Integration in Databases: How to automatically give a global view to 

information available in heterogeneous and distributed databases. Techniques have been 

developed to handle differing schemas and terminology. 

2. Ontologies and languages and tools to support their use: How to formally define the 

meaning of terminologies so that varying terminologies can be manipulated by machines. 

3. Semantic Web: The development of languages to make information available on the web 

(both web pages and web services) interpretable by machines. 

This is not to say that work in the above areas is completed. There is still much to be done. But 

some problems have been isolated, and reasonable solutions have been developed. Work in 

these areas is mature enough to begin the process of technology transition for some aspects of the 

knowledge fusion problem. 

Integration of the high-level language-oriented reasoning methods and the data oriented 

mathematical techniques of data fusion remains a fundamental research problem. Some potential 

useful approaches (pointed out earlier) are the work of D. Brown and collaborators on 

incorporating world knowledge into techniques for tracking objects and also the work of 

D. Koller, A. Pfeffer, and others on creating probabilistic versions of a number of 

knowledge-representation languages. More work is needed on integrating the information 

obtainable from these different levels. 

A great deal of fundamental research in architectures for data fusion remains to be done. 

Specific approaches for developing large-scale, distributed architectures that will support the 

Future Force vision at all force levels needs to be researched. Work in the following areas can 

be drawn upon: 

1. Agents: How to build systems of multiple interacting programs. 

2. Grids: How to perform distributed computations. 
28

3. Web Services: Incorporation of services into the semantic web. 

4. Distributed Systems: How to create robust distributed architectures. 

5. Streaming Data: How to summarize, simplify, identify critical change and critical objects 

in real-time streams of sensor, video, audio, messaging data. 

6. Information Integration: How to design and manage the integration of heterogeneous 

application components and their interactions. 

There are a number of areas of reasoning and representation in which more work is needed for 

successful knowledge fusion: 

1. The determination of the goals of the various human entities (e.g., enemy forces) is a major 

problem, and the state of current knowledge is not sufficient to easily address the problem. 

On the basis of available evidence, it is necessary to identify the likely plan of different 

agents in the battlesphere. More work is needed in plan recognition, situation, impact 

assessment and awareness, and abductive reasoning. These issues are clearly related to the 

handling of uncertainty and also the representation of information. 

2. It is not clear how to combine the lower level sensor readings and the statistical data fusion 

predictions with the higher level representation of plans, real-time information from the 

web and other sources. More work is needed to address this issue both by developing 

appropriate fusion architectures and new forms of representation, including methods for 

representing audio and video content. 

3. Work is needed on how to properly integrate textual information into the overall CROP. 

This information may, for example, be reports of human observers. 

4. More work is needed on how to handle uncertainty in this setting. Information may be 

conflicting. It is necessary to manage multiple competing hypotheses and update the 

probabilities of each based upon newly arriving information. 

5. A flexible method of representing actions, decisions, and plans is needed. This includes 

both the hypothesized actions and plans of the enemy forces and representations to be 

given to agents or battlespace entities. One of the ideas behind NCW is that commanders 

do not have to issue top-down detailed instructions, but can rely on the local units to act in 

accordance with their knowledge to achieve the desired behavior. But the specification 

(and monitoring) of the action or plan to be given to such a system of knowledgeable actors 

is an open question. 
29

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