Mieczyslaw M. Kokar

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.

UML for ontology development

Ontologies are becoming increasingly important because they provide the critical semantic foundation for many rapidly expanding technologies such as software agents, e-commerce and knowledge management (McGuinness, 2002). The Unified Modelling Language (UML)1 has been widely adopted by the software engineering community and its scope is broadening to include more diverse modelling tasks. This paper discusses the recent convergence of UML and ontologies and suggests some possible future directions.

Extending UML to Support Ontology Engineering for the Semantic Web

There is rapidly growing momentum for web enabled agents that reason about and dynamically integrate the appropriate knowledge and services at run-time. The World Wide Web Consortium and the DARPA Agent Markup Language (DAML) program have been actively involved in furthering this trend. The dynamic integration of knowledge and services depends on the existence of explicit declarative semantic models (ontologies). DAML is an emerging language for specifying machine-readable ontologies on the web. DAML was designed to support tractable reasoning.We have been developing tools for developing ontologies in the Unified Modeling Language (UML) and generating DAML. This allows the many mature UML tools, models and expertise to be applied to knowledge representation systems, not only for visualizing complex ontologies but also for managing the ontology development process. Furthermore, UML has many features, such as profiles, global modularity and extension mechanisms that have yet to be considered in DAML.Our paper identifies the similarities and differences (with examples) between UML and DAML. To reconcile these differences, we propose a modest extension to the UML infrastructure for one of the most problematic differences. This is the DAML concept of property which is a first-class modeling element in DAML, while UML associations are not. For example, a DAML property can have more than one domain class. Our proposal is backward-compatible with existing UML models while enhancing its viability for ontology modeling.While we have focused on DAML in our research and development activities, the same issues apply to many of the knowledge representation languages. This is especially the case for semantic network and concept graph approaches to knowledge representations.

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.

Extending the Unified Modeling Language for ontology development

Abstract.There is rapidly growing momentum for web enabled agents that reason about and dynamically integrate the appropriate knowledge and services at run-time. The dynamic integration of knowledge and services depends on the existence of explicit declarative semantic models (ontologies). We have been building tools for ontology development based on the Unified Modeling Language (UML). This allows the many mature UML tools, models and expertise to be applied to knowledge representation systems, not only for visualizing complex ontologies but also for managing the ontology development process. UML has many features, such as profiles, global modularity and extension mechanisms that are not generally available in most ontology languages. However, ontology languages have some features that UML does not support. Our paper identifies the similarities and differences (with examples) between UML and the ontology languages RDF and DAML+OIL. To reconcile these differences, we propose a modification to the UML metamodel to address some of the most problematic differences. One of these is the ontological concept variously called a property, relation or predicate. This notion corresponds to the UML concepts of association and attribute. In ontology languages properties are first-class modeling elements, but UML associations and attributes are not first-class. Our proposal is backward-compatible with existing UML models while enhancing its viability for ontology modeling. While we have focused on RDF and DAML+OIL in our research and development activities, the same issues apply to many of the knowledge representation languages. This is especially the case for semantic network and concept graph approaches to knowledge representations.

High Level Information Fusion (HLIF): Survey of models, issues, and grand challenges

High-level information fusion (situation and threat assessment, process and user refinement) requires novel solutions for the operational transition of information fusion designs. Low-level (signal processing, object state estimation and characterization) is well-vetted in the community as compared to high-level information fusion (control and relationships to the environment). Specific areas of interest include modeling (situations, environments), representations (semantic, knowledge, and complex), information management (ontologies, protocols) systems design (scenario-based, user-based, distributed-agent) and evaluation (measures of performance/effectiveness, and empirical case studies).

Formalizing classes of information fusion systems

This paper provides an outline of a formalization of classes of information fusion systems in terms of category theory and formal languages. The formalization captures both the inputs/outputs of a fusion system and the fusion processing algorithms. The paper also introduces a notion of subclass, which is used to compare classes of fusion systems, whether they are different or one is a special case of another. Two examples of classes of fusion systems formalized in the paper are data fusion and decision fusion; decision fusion is shown to be a subclass of data fusion. A number of other classes of fusion systems are defined. The formalization is extended by adding the notion of measure of effectiveness, which is then used to prove that one of the classes (so called overlapping system) is at least as efficient as a single-source system. And finally it is shown how data association can be formalized in this framework. While at first the formalization could be used by information fusion scientists to formally define various types of fusion systems and then to prove theorems about properties of such systems, it is expected that it should lead to the development of tools that could be used by software engineers to formally derive designs of fusion systems.

Determining Arguments of Invariant Functional Descriptions

In this paper we examine the problem of determining arguments of invariant functional descriptions from incomplete observational data. Physical laws are one example of invariant functional descriptions. For such functions, we show that one can test the relevance of the functions arguments even though their values remain constant throughout the observational data. We present a method, called COPER, for discovering invariant functional descriptions. COPER eliminates irrelevant arguments, generates additional relevant arguments, and generates a functional formula. We focus on the first two of these features, giving two examples of how the methodology can be applied to determining arguments of physical laws.

Issues and challenges of knowledge representation and reasoning methods in situation assessment (Level 2 Fusion)

Situation assessment (SA) involves deriving relations among entities, e.g., the aggregation of object states (i.e. classification and location). While SA has been recognized in the information fusion and human factors literature, there still exist open questions regarding knowledge representation and reasoning methods to afford SA. For instance, while lots of data is collected over a region of interest, how does this information get presented to an attention constrained user? The information overload can deteriorate cognitive reasoning so a pragmatic solution to knowledge representation is needed for effective and efficient situation understanding. In this paper, we present issues associated with Level 2 (Situation Assessment) including: (1) user perception and perceptual reasoning representation, (2) knowledge discovery process models, (3) procedural versus logical reasoning about relationships, (4) user-fusion interaction through performance metrics, and (5) syntactic and semantic representations. While a definitive conclusion is not the aim of the paper, many critical issues are proposed in order to characterize future successful strategies to knowledge representation and reasoning strategies for situation assessment.

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.