Christopher J. Matheus

• PhD Computer Science, UIUC
• Independent Consultant, AI/SemTech/WebApps

To accommodate the proliferation of heterogeneous network models and protocols, the use of semantic technologies to enable an abstract treatment of networks is proposed. Network adapters are employed to lift network specific data into a semantic representation. Semantic reasoning integrates the disparate network models and protocols into a common d...

To accommodate the proliferation of heterogeneous network models and protocols we propose the use of semantic technologies to enable an abstract treatment of networks. Network adapters are employed to lift network specific data into a semantic representation that is grounded in an upper level "NetCore" ontology. Semantic reasoning integrates the di...

Semantic technologies are increasingly being employed to integrate, relate and classify heterogeneous data from various problem domains. To date, however, little empirical analysis has been carried out to help identify the benefits and limitations of different semantic approaches on specific data integration and classification problems. This paper...

One of the promises of the Semantic Web is to support applications that easily and seamlessly deal with heterogeneous data. Most data in the Web, however, is in the Extensible Markup Language (XML) format, but using XML requires applications to understand the format of each data source that they access. Achieving the benefits of the Semantic Web in...

One of the promises of the Semantic Web is to support applications that easily and seamlessly deal with heterogeneous data. Most data on the Web, however, is in the Extensible Markup Language (XML) format, but using XML requires applications to understand the format of each data source that they access. To achieve the benefits of the Semantic Web i...

This paper describes the implementation of a Smart Campus application prototype that integrates heterogeneous data using semantic technologies. The prototype is based on a layered semantic architecture that facilitates semantic data access and integration using OWL, SWRL and SPARQL. The focus of the paper is on the prototype implementation and the...

Integrating and relating heterogeneous data using inference is one of the cornerstones of semantic technologies and there are a variety of ways in which this may be achieved. Cross source relationships can be automatically translated or inferred using the axioms of RDFS/OWL, via user generated rules, or as the result of SPARQL query result transfor...

We say that a computer program augments the analyst if it can infer facts that are implicit in existing information, but that may be relatively difficult for a human to infer. Among a multitude of reasons, the analyst's task is difficult because (1) reported information to be analyzed and reasoned about often cannot be completely trusted (requiring...

Doctrinally, Priority Intelligence Requirements (PIRs) represent information that the commander needs to know in order to make a decision or achieve a desired effect. Networked warfare provides the intelligence officer with access to multitudes of sensor outputs and reports, often from unfamiliar sources. Counterinsurgency requires evaluating infor...

Report available at http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA519041

The authors' automated framework evaluates blog posts in a social-political blogosphere and, by aggregation, entire blogs according to their relevance, specificity, timeliness, and credibility. These metrics are superior to current methods in information retrieval for blogs because they better reflect the distinctive hyperlink structure of a social...

The blogosphere provides a novel window into public opinion, but its dynamic nature makes it an elusive medium to analyze and interpret in the aggregate, where it is most informative. We are developing new technology employing ontologies to solve this problem by fusing the signals of the blogosphere and zeroing in on issues that are most likely to...

Enemy or threat courses of action are produced during intelligence preparation of the battlefield, during the military decision making process, and as part of the process of situation development. Due to the overwhelming amount of information involved in these processes and the limited time available to intelligence analysts, significant efforts ar...

During joint operations among multi-national forces it is imperative that the planned courses of action (COA) of coalition units be accurately and precisely communicated between battlefield operating systems, particularly when dealing with highly coordinated maneuvers. A similar need arises in being able to communicate intelligence concerning hypot...

The notions of “situation” and “situation awareness” have been formulated by many authors in various contexts. In this paper, we present a formalization of situations that is compatible with the interpretation of situation awareness in terms of human awareness as well as the situation theory of Barwise and Devlin. The purpose of this paper is to ca...

Situation awareness makes a step forward from the previous focus on Level 1 information processing and fusion, as defined by the JDL model. However, in most approaches the meaning of dasiasituation awarenesspsila is limited to knowing whether a particular relation holds or not. In our approach, situations are considered as entities that can affect,...

In this paper we present our experience working on the problem of detecting suspicious activity using OWL ontologies and inference rules. For this purpose we implemented partial solutions using three different rule engines - BaseVISor, Jena and Jess. Each of them required different levels of effort and each had its strengths and weaknesses. We desc...

This demonstration will show the practical application of a formal reasoning engine for both policy invocation and policy reconciliation. The primary features of this demonstration include the use of formal ontologies, context based policy reasoning, and support for both policy invocation and reconciliation. BaseVISor [C. Matheus et al., 2006], a f...

Enemy Courses of Action (ECOAs) play a central role in the process of situation development in military decision-making. In order to reason about ECOAs, it would be necessary to adequately represent them in a formalism that allows for automatic reasoning. In this paper, we examine the benefits and drawbacks of representing ECOAs within several fram...

The JC3IEDM is a data model intended for the exchange of command, control and communication information. It is available as an ERwin data model for which there is an XML-based description of all entities, attributes, relations and codes, making it ripe for translation by XSLT. This paper describes the development of a set of transformation scripts...

BaseVISor is a forward-chaining inference engine based on a Rete network optimized for the processing of RDF triples. A clause within the body and head of a rule either represents an RDF triple or invokes a procedural attachment (either built-in or user defined). This paper describes how BaseVISor has been outfitted to process RuleML and R-Entailme...

A key challenge presented by the increasing volume and complexity of information collectable from battlespace situations is the development of intelligent applications able to automatically analyze the information and identify critical enemy activities in a timely manner. What makes this information fusion problem particularly difficult is the stro...

This paper is an introduction to the Workshop on Situation Management, SIMA 2005. We discuss the scope of the workshop, the big picture of situation management, and a summarization of the papers selected for inclusion in the workshop. Topics include situation knowledge acquisition, learning & situation recognition, structural & behavioral modeling...

When developing situation awareness applications we begin by con- structing an OWL ontology to capture a language of discourse for the domain of interest. Such an ontology, however, is never sufficient for fully representing the complex knowledge needed to identify what is happening in an evolving situation - this usually requires general implicati...

Situation awareness involves the identification and monitoring of relationships among objects participating in an evolving situation. This problem in general is intractable (i.e., there is a potentially infinite number of relations that could be tracked) and thus requires additional constraints and guidance defined by the user if there is to be any...

SAWA is a situation awareness assistant being developed by Versatile Information Systems, Inc. During the process of its development, several lessons were learned about advantages and limitations of certain approaches, techniques, and technologies as they are applied to situation awareness. This paper begins with an overview of SAWA and then focuse...

This paper describes the authors’ efforts to develop a pedigree ontology for level- one sensor fusion. This work was performed in the context of naval operations but the concepts employed are applicable to any domain involving sensor fusion. The ontology that has been developed is formally represented using OWL, the Web Ontology Language used in de...

Situation awareness involves the identification and monitoring of relationships among level-one objects. This problem in general is intractable (i.e., there is a potentially infinite number of relations that could be tracked) and thus requires additional constraints and guidance defined by the user if there is to be any hope of creating practical s...

Versatile Information Systems, Inc. (VIS) has been applying Semantic Web technologies to the problems of situation awareness and information fusion for more than five years. Situation awareness involves the real-time processing of event-based information coming from an evolving situation in an attempt to understand what is happening. In our view, s...

RuleML and SWRL are closely related rule markup languages that are growing in popularity particularly within the Semantic Web community. Both are based on XML and as such are more amenable to computer processing than human generation and interpretation. Even so it is possible to read and write RuleML and SWRL rules manually using a text editor. Due...

As the use of Semantic Web ontologies continues to expand there is a growing need for tools that can validate ontological consistency and provide guidance in the correction of detected defects and errors. A number of tools already exist as evidenced by the ten systems participating in the W3C's evaluation of the OWL Test Cases. For the most part, t...

Ontologies are becoming increasingly popular due to recent e#orts to extend the capabilities of the World Wide Web through the addition of formal semantics. While ontologies have traditionally been used as precise languages to facilitate e#cient exchange of information among people, the "Semantic Web" is extending this role to software agents. For...

After a number of years of intensive research on Level 1 fusion, the focus is shifting to higher levels. Level 2 fusion differs from Level 1 fusion in its emphasis on relations among objects rather than on the characteristics (position, velocity, type) of single objects. While the number of such characteristics grows linearly with the number of obj...

The grand vision of the Semantic Web has led to the development of new technologies intended to serve as the groundwork for its eventual realization. Primary among these technologies is the Web ontology language OWL [1] and its more recent extension in the form of the Semantic Web rule language SWRL [2]. These languages are built upon decades of re...

Situation Awareness involves the comprehension of the state of a col- lection of objects in an evolving environment. This not only includes an under- standing of the objects' characteristics but also an awareness of the significant relations that hold among the objects at any point in time. Systems for estab- lishing situation awareness require a k...

This paper describes a case study of relation derivation within the context of situation awareness. First we present a scenario in which inputs are supplied by a simulated Level 1 system. The inputs are events annotated with terms from an ontology for situation awareness. This ontology contains concepts used to represent and reason about situations...

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Situation awareness means simply that one knows what is going on around oneself. In operational terms, this means that one knows the information that is relevant to a task. Maintaining a coherent awareness of the situation is essential to successful task completion. We propose a formal basis for situation awareness that draws on sources and makes u...

The analysis of network traffic and customer usage patterns is critical for the network operation, capacity planning and targeted marketing of the cellular industry. This data analysis task presents both an opportunity and a challenge for data warehousing technology because of the huge amount of wireless calling data and the dynamic nature of the u...

Event correlation is a widely accepted technology for managing the complexity of modern telecommunication and data networks. This paper outlines the directions for future advancement of the technology and practice of event correlation systems. It shows that event correlation solutions are an integral part of the next generation of network managemen...

In response to shifting business demands, evolving information technologies play a key role in first reengineering and then reinventing GTE (telecommunications company). To illustrate the growing impact of IT within GTE, we chronicle the evolution of GTE's Network Management System (NMS). Although we focus primarily on the NMS, we broadly identify...

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The automated discovery of knowledge in databases is becoming increasingly important as the world's wealth of data continues to grow exponentially. Knowledge-discovery systems face challenging problems from real-world databases which tend to be dynamic, incomplete, redundant, noisy, sparse, and very large. This paper addresses these problems and de...

This paper presents two methods for adding domain knowledge to similarity-based learning through feature construction, a form of representation change in which new features are constructed from relationships detected among existing features. In the first method, domain-knowledge constraints are used to eliminate less desirable new features before t...

The Key Findings Reporter (KEFIR) is a system for discovering and explaining "key findings" in large, relational databases. This paper describes an application of KEFIR to the analysis of health-care information. The system performs an automatic analysis of data along multiple dimensions to determine the most interesting deviations of specific quan...

One of the most promising areas in Knowledge Discovery in Databases is the automatic analysis of changes and deviations. Several systems have recently been developed for this task. Success of these systems hinges on their ability to identify a few important and relevant deviations among the multitude of potentially interesting events. In this paper...

Selective induction techniques perform poorly when the features are inappropriate for the target concept. One solution is to have the learning system construct new features automatically; unfortunately feature construction is a difficult and poorly understood problem. In this paper we present a definition of feature construction in concept learning...

The Key Findings Reporter (KEFIR) is a system for discovering and explaining "key findings" in large, relational databases. This paper describes an application of KEFIR to the analysis of health-care information. The system performs an automatic analysis of data along multiple dimensions to determine the most interesting deviations of specific quan...

We present a method for learning higher-order polynomial functions from examples using linear regression and feature construction. Regression is used on a set of training instances to produce a weight vector for a linear function over the feature set. If this hypothesis is imperfect, a new feature is constructed by forming the product of the two fe...

Information by itself is a pretty thin meal, if not mixed with other ingredients. { Internet quote One of the most promising areas in Knowledge Discovery in Databases is the automatic analysis of deviations. Success in this task hinges on the ability to identify a few important and relevant events among the multitude of potentially interesting devi...

An abstract is not available.

n Over 60 researchers from 10 countries took part in the Third Knowledge Dis-covery in Databases (KDD) Workshop, held during the Eleventh National Con-ference on Artificial Intelligence in Washington, D. C. A major trend evi-dent at the workshop was the transition to applications in the core KDD area of discovery of relatively simple patterns in re...

Knowledge-discovery systems face challenging problems from real-world databases, which tend to be dynamic, incomplete, redundant, noisy, sparse, and very large. These problems are addressed and some techniques for handling them are described. A model of an idealized knowledge-discovery system is presented as a reference for studying and designing n...

We describe the Knowledge Discovery Workbench, an interactive system for database exploration. We then illustrate KDW capabilities in data clustering, summarization, classification, and discovery of changes. We also examine extracting dependencies from data and using them to order the multitude of data patterns. © 1992 John Wiley & Sons, Inc.

this article. 0738-4602/92/$4.00 1992 AAAI 58 AI MAGAZINE for the 1990s (Silberschatz, Stonebraker, and Ullman 1990)

This paper illustrates how many empirical-based learning systems are adversely affected by disjunctive regions in instance space. Constructive induction of new features is proposed as a way to overcome the problems caused by disjuncts. A simple model for feature construction is presented with examples drawn from existing systems.

This paper presents two methods for adding domainknowledge to similarity-based learning through featureconstruction, a form of representation change in whichnew features are constructed from relationships detectedamong existing features. In the first method,domain-knowledge constraints are used to eliminateless desirable new features before they ar...

Concept learning from examples can be relatively easy if the training set is represented in a suitable form. However, when the features used in describing the examples are inappropriate for the target concept, learning can be difficult or impossible using selective induction methods. This chapter presents a framework for constructive induction base...

Printout. Thesis (Ph. D)--University of Illinois at Urbana-Champaign, 1990. Vita. Includes bibliographical references (leaves 159-168). Available on microfilm from University Microfilms.

In cognitive psychology and machine learning, concept representation models are commonly classified into three types: defining feature models, prototype models, and exemplar-based models. In the defining features or classical model, concepts are lists of singularly necessary and jointly sufficient features. Prototype models represent concepts as li...

This paper presents an overview and analysis of teaming in artificial neural systems (ANSs). It begins with a general introduction to neural networks and connectionist approaches to information processing. The basis for learning in ANSs is then described and compared with classical machine learning. While similar in some ways, ANS learning deviates...

Two distinct programming methodologies - production systems and FORTH - have been combined to form a unique system called FORPS. FORPS (FORth-based Production System) was developed to combine the intelligent, rule-based control of production systems with the real-time control capabilities of FORTH. It is a complete production system offering high s...

FORPS is a Forth-based Production System developed for fast, real time control problems. It employs a very small and efficient inference engine to cycle through sets of IF-THEN production rules. The original version of FORPS was written in polyFORTH for a 68000 microcomputer. Recently, FORPS has been ported to the NOVIX Beta Board running NOVIX FOR...

A simple yet very powerful system has been developed that merges the artificial intelligence qualities of a production system with the real-time control capabilities of FORTH. FORPS (FORTH-based Production System) offers the advantages of intelligent, rule-based control in a small package offering high speed, extensibility, and simplicity. A practi...

Commanders require relevant information about background information in order to exercise effective command and control (C2). METT-TC factors (Mission, Enemy, Terrain & Weather, Troops, Time Available and Civil Considerations) represent the canonical, militarily significant background against which information is evaluated and military decisions ar...