Abstract- 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.

Sponsor: ESC Contract No.: FA9721-04-0001

Abstract- The strategy of data fusion has been applied in threat prediction and situation awareness and the terminology has been standardized by the Joint Directors of Laboratories (JDL) in the form of a socalled JDL Data Fusion Model, which currently called DFIG model. Higher levels of the DFIG model call for prediction of future development and awareness of the development of a situation. It is known that Bayesian Network is an insightful approach to determine optimal strategies against asymmetric adversarial opponent. However, it lacks the essential adversarial decision processes perspective. In this paper, a highly innovative data-fusion framework for asymmetricthreat detection and prediction based on advanced knowledge infrastructure and stochastic (Markov) game theory is proposed. In particular, asymmetric and adaptive threats are detected and grouped by intelligent agent and Hierarchical Entity Aggregation in Level 2 and their intents are predicted by a decentralized Markov (stochastic) game model with deception in Level 3. We have verified that our proposed algorithms are scalable, stable, and perform satisfactorily according to the situation awareness performance metric.

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 this to be possible, ontologies must be formalized in languages processable by computers, such as OWL, the W3C's Web Ontology Language. The purpose of OWL ontologies is to permit software agents to understand web content and to interact intelligently with Web services (which may themselves be software agents). The use of such ontologies, however, need not be constrained to the Web. Recently, ontologies have found their way into higher-level information fusion where they are providing a means for describing and reasoning about sensor data, objects, relations and general domain theories. To the best of our knowledge, there is as of yet no documented e#ort to capture the main uses of ontologies in information fusion. In this paper we start filling this void by presenting a number of "use cases," i.e., scenarios of the use of ontologies in the context of higher-level information fusion. In this paper we develop use cases in which ontologies are used both for the fusion process itself and for the development of fusion systems. The use cases cover scenarios in which the agent roles are played by people, software or both.

Abstract. Situation Awareness involves the comprehension of the state of a collection of objects in an evolving environment. This not only includes an understanding of the objects ’ characteristics but also an awareness of the significant relations that hold among the objects at any point in time. Systems for establishing situation awareness require a knowledge representation for these objects and relations. Traditional ontologies, as defined with a language like DAML/OWL, are commonly used for such purposes. Unfortunately, these languages are insufficient for describing the conditions under which specific relations might hold true, which requires the explicit representation of implications, as is provided by RuleML. This paper describes an approach to knowledge representation for situation awareness employing RuleML-based domain theories constructed over OWL ontologies, presented in the context of its implementation in a Situation Awareness Assistant under development by the authors. Suggestions are also made for additions to the RuleML specification. 1

Abstract. Situation awareness involves the identification of relationships among objects participating in an evolving situation. This problem in general is intractable 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) based on Semantic Web technologies 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 a 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 presented along with a discussion of the challenges encountered in using SWRL for this task. 1

Abstract. Ontologies enable explicit expression of collective concepts and support Machine-to-Machine (M2M) interactions at the semantic level. Ontologies expressed in a standard language, such as the Web Ontology Language (OWL) and exposed on a network offer the potential for unprecedented interoperability solutions since they are semantically rich, computer interpretable and inherently extensible. In this paper, we describe how we applied ontologies in OWL for rapid enterprise integration of heterogeneous data sources. We found that once a robust foundational domain ontology is established, it is easy and quick to integrate new data sources and therefore rapidly provide new system capabilities.

Abstract –Subsequent revisions to the JDL model modified definitions for model usefulness that stressed differentiation between fusion (estimation) and sensor management (control). Two diverging groups include one pressing for fusion automation (JDL revisions) and one advocating the role of the user (User-Fusion model). The center of debate is real-world delivery of fusion systems which requires presenting information fusion results for knowledge representation (fusion estimation) and knowledge reasoning (control management). The purpose of the paper is to highlight the need of Users, with individual differences, facilitated by knowledge representations to reason about user situational awareness (SA). This position paper highlights: (1) Addressing the user in system management / control (2) Assessing information quality (metrics) to support SA (3) Evaluating Fusion systems to deliver user info needs, (4) Planning knowledge delivery for dynamic updating (5) Designing SA interfaces to support user reasoning

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, situation awareness primarily comes down to identifying higher-order relations that come into being within a situation and that have particular relevance to the problem at hand as defined by the user’s goals or objectives 1, 2. By higher-order relations we mean relations involving multiple objects; OWL ObjectProperties represent the simplest of such relations involving two objects but situation awareness is often interested in more complex relations involving several objects. We contrast these higher-order relations with those that merely define characteristics of an individual object; DataProperties fall into this category. The analytical processes that go into establishing situation awareness necessarily involve information fusion, the processes by which data/information from multiple sources are combined to produce new enhanced information that incorporates aspects of the raw, original sources. Our approach to both situation awareness and data fusion involves the use of formal ontologies