Christopher J. Matheus

A core ontology for situation awareness

In this paper we present an ontology for situation awareness. One of our goals is to support the claim that this ontology is a reasonable candidate for representing various scenarios of situation awareness. Towards this aim we provide an explanation of the meaning of this ontology, show its expressiveness and demonstrate its extensibility. We also compare the expressiveness of this ontology with alternative approaches we considered during the design of the ontology. We then show how the ontology can be adapted to handle domain-specific situations by readily extending the core language. The extensions include adding subclasses, sub-properties and additional attributes to the core ontology. We conclude with an example of how the ontology can be used to annotate specific instances of a situation.

SAWA: an assistant for higher-level fusion and situation awareness

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 situation awareness systems. This paper describes a Situation Awareness Assistant (SAWA) that facilitates the development of user-defined domain knowledge in the form of formal ontologies and rule sets and then permits the application of the domain knowledge to the monitoring of relevant relations as they occur in evolving situations. SAWA includes tools for developing ontologies in OWL and rules in SWRL and provides runtime components for collecting event data, storing and querying the data, monitoring relevant relations and viewing the results through a graphical user interface. An application of SAWA to a scenario from the domain of supply logistics is also presented.

Derivation of ontological relations using formal methods in a situation awareness scenario

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. The ontology and the annotations of events are represented in DAML and Rule-ML and then systematically translated to a formal method language called MetaSlang. Having all information expressed in a formal method language allows us to use a theorem prover, SNARK, to prove that a given relationship among the Level 1 objects holds (or that it does not hold). The paper shows a proof of concept that relation derivation in situation awareness can be done within a formal framework. It also identifies bottlenecks associated with this approach, such as the issue of the large number of potential relations that may have to be considered by the theorem prover. The paper discusses ways of resolving this as well as other problems identified in this study.

GRACE: building next generation event correlation services

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 management systems, which will be based on distributed service (component) architecture. We present this architecture, and discuss the importance of the unification of data and knowledge presentation formalisms, and the use of advanced technologies such as CORBA, Java, and XML. Finally, we demonstrate how these technological solutions have been implemented in the distributed event correlation system called GRACE /spl lambda/obal Real-Time Advanced Correlation Environment.

Towards a symptom ontology for semantic web applications

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 W3Cs evaluation of the OWL Test Cases. For the most part, these first generation tools focus on experimental approaches to consistency checking, while minimal attention is paid to how the results will be used or how the systems might interoperate. For this reason very few of these systems produce results in a machine-readable format (for example as OWL annotations) and there is no shared notion across the tools of how to identify and describe what it is that makes a specific ontology or annotation inconsistent. In this paper we propose the development of a Symptom Ontology for the Semantic Web that would serve as a common language for identifying and describing semantic errors and warnings that may be indicative of inconsistencies in ontologies and annotations; we refer to such errors and warnings as symptoms. We offer the symptom ontology currently used by the ConsVISor consistency-checking tool, as the starting point for a discussion on the desirable characteristics of such an ontology. Included among these characteristics are 1) a hierarchy of common symptoms, 2) clear associations between specific symptoms and the axioms of the languages they violate and 3) a means for relating individual symptoms back to the specific constructs in the input file(s) through which they were implicated. We conclude with a number of suggestions for future directions of this work including its extension to syntactic symptoms.

Transforming XML Schema to OWL Using Patterns

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 involves transforming XML into the Semantic Web language, OWL (Ontology Web Language), a process that generally has manual or only semi-automatic components. In this paper we present a set of patterns that enable the direct, automatic transformation from XML Schema into OWL allowing the integration of much XML data in the Semantic Web. We focus on an advanced logical representation of XML Schema components and present an implementation, including a comparison with related work.

Formalization of Situation Awareness

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 use of techniques from the logic, human-computer interaction and data fusion communities. Our framework includes formalizations of the data fusion process as well as the notion of a situation. We express our formalization using various languages, including UML, DAML and the Slang formal methods language, each of which has its own unique contribution to our framework.

Overview of situation management at SIMA 2005

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 of sensor networks, robotic sensors & mobile sensor grids, advanced architectures for situation awareness, and human-centric situation management. We conclude with a discussion of hard, outstanding challenges in situation management and future R&D areas

Association in Level 2 Fusion

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 objects considered by an information fusion system, this cannot be said about the number of possible relations, which can grow exponentially. To alleviate the problems of computational complexity in Level 2 processing, the authors of this paper have suggested the use of ontologies. In this paper we analyze the issue of association in Level 2 fusion. In particular, we investigate ways in which the use of ontologies and annotations of situations in terms of the ontologies can be used for deciding which of the objects, and/or relations among such, can be considered to be the same. This is analogous to data association in Level 1 fusion. First, we show the kinds of reasoning that can be carried out on the annotations in order to identify various objects and possible coreferences. Second, we analyze how uncertainty information can be incorporated into the process. The reasoning aspect depends on the features of the ontology representation language used. We focus on OWL - the web ontology language. This language comprises, among others, constructs related to expressing multiplicity constraints as well as such features like “functional property” and “inverse functional property”. We will show how these features can be used in resolving the identities of objects and relations. Moreover, we will show how a consistency-checking tool (ConsVISor) developed by the authors can be used in this process.

Lessons learned from developing SAWA: a situation awareness assistant

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 focuses on some of the more significant lessons learned. These include the pros and cons of leveraging semantic Web technologies, the handling of time-varying attributes, and the processing of uncertainty.