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.
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.
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.
Conference Committee Involvement (1)
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2008