TMMT: Tool Supporting Knowledge Modelling

International Journal of Human-Computer Studies, 2000

The aim of the paper is to discuss the use of knowledge models to formulate general applications. First, the paper presents the recent evolution of the software field where increasing attention is paid to conceptual modeling. Then, the current state of knowledge modeling techniques is described where increased reliability is available through the modern knowledge acquisition techniques and supporting tools. The KSM (Knowledge Structure Manager) tool is described next. First, the concept of knowledge area is introduced as a building block where methods to perform a collection of tasks are included together with the bodies of knowledge providing the basic methods to perform the basic tasks. Then, the CONCEL language to define vocabularies of domains and the LINK language for methods formulation are introduced. Finally, the object oriented implementation of a knowledge area is described and a general methodology for application design and maintenance supported by KSM is proposed. To illustrate the concepts and methods, an example of system for intelligent traffic management in a road network is described. This example is followed by a proposal of generalization for reuse of the resulting architecture. Finally, some concluding comments are proposed about the feasibility of using the knowledge modeling tools and methods for general application design.

2000

The aim of the paper is to discuss the use of knowledge models to formulate general applications. First, the paper presents the recent evolution of the software field where increasing attention is paid to conceptual modeling. Then, the current state of knowledge modeling techniques is described where increased reliability is available through the modern knowledge acquisition techniques and supporting tools. The KSM (Knowledge Structure Manager) tool is described next. First, the concept of knowledge area is introduced as a building block where methods to perform a collection of tasks are included together with the bodies of knowledge providing the basic methods to perform the basic tasks. Then, the CONCEL language to define vocabularies of domains and the LINK language for methods formulation are introduced. Finally, the object oriented implementation of a knowledge area is described and a general methodology for application design and maintenance supported by KSM is proposed. To illustrate the concepts and methods, an example of system for intelligent traffic management in a road network is described. This example is followed by a proposal of generalization for reuse of the resulting architecture. Finally, some concluding comments are proposed about the feasibility of using the knowledge modeling tools and methods for general application design.

Knowledge modelling techniques are widely adopted for designing knowledge-based systems (KBS) and these systems are important for knowledge management. This paper argues the need and importance of adopting Unified Modeling Language (UML) for knowledge modelling through the profile extension mechanism. KBS are developed using knowledge engineering (KE) techniques that are often similar to software engineering (SE), and both domains emphasise the use of conceptual models in designing software systems. Most of these knowledge models heavily rely on modelling notations used in software engineering; here the UML is widely adopted. UML can be formally tailored for knowledge modelling by creating a UML profile for knowledge modelling.

Communications of the ACM, 1992

Analysis for Knowledge MOdeling n recent years there has been increasing interest in describing complicated information processing systems in terms of the knowledge they have, rather than by the details of their implementation. This requires a means of modeling the knowledge in a system. Several different approaches to knowledge modeling have been developed by researchers working in Artificial Intelligence (AI). Most of these approaches share the view that knowledge must be modeled with respect to a goal or task. In this article, we outline our modeling approach in terms of the notion of a task-structure, which recursively links a task to alternative methods and to their subtasks. Our emphasis is on the notion of modeling domain knowledge using tasks and methods as mediating concepts. We begin by tracing the development of a number of different knowledge-modeling approaches. These approaches share many features, but their differences make it difficult to compare systems that have been modeled using different approaches. We present these approaches and describe their similarities and differences. We then give a detailed description, based on the task structure, of our knowledge-modeling approach and illustrate it with task structures for diagnosis and design. Finally, we show how the task structure can be used to compare and unify the other approaches. ® ® ® ® ® ® ® ® • ® ® ® • ® ® Knowledge-Based Systems: What are they?

1996

The paper describes the MIKE (Model-based and Incremental Knowledge Engineering) approach for the development of knowledge-based systems (kbs). It integrates semifonnal specification techniques, formal specification techniques, and prototyping into a coherent framework. This allows the domain and task model of a kbs to be described on different formalization levels. All activities in the building process are embedded in a cyclic life cycle model. For the semiformal representation we use a hypermedia-based fonnalism which serves as a communication basis between expert and knowledge engineer during knowledge acquisition. The semiformal knowledge representation is also the basis for formalization, resulting in a formal and executable model of expertise specified in the Knowledge Acquisition and Representation Language (KARL). Since KARL is executable the model of expertise can be developed and validated by prototyping. A smooth transition from a semiformal to a formal specification and further on to design is achieved as all the description techniques rely on the same conceptual model to describe the functional and non-functional aspects of the system. Thus, the system is thoroughly documented at different description levels, each of which focuses on a distinct aspect of the entire development effort. Traceability of requirements is supported by linking the different models to each other. Though the MIKE approach aims at supporting the building process of kbs, its principles and methods apply also to the development of non-knowledge-based software systems, e.g. information systems.