Evaluating a formal KBS specification language

In recent years, the knowledge engineering community has begun to explore formal specification languages as a tool in the development of knowledge-based systems. These formal knowledge modelling languages have a number of advantages over informal languages, such as their precise meaning and the possibility to derive properties through formal proofs. However, these formal languages also suffer from problems which limit their practical usefulness: they are often not expressive enough to deal with real world applications, formal models are complex and hard to read, and constructing a formal model is a difficult, error prone and expensive process. The goal of the study presented in this paper is to investigate the usability of one such formal {KBS} modelling language, called (ML)^2. (ML)^2 is strongly based on the structure of the knowledge-models used in the KADS KBS development method. We first designed a set of evaluation criteria. We then applied (ML)^2 in two case-studies and scored the language on our evaluation criteria. (ML)^2 scored well on most of our criteria. This leads us to conjecture that the close correspondence between the informal KADS models and the formal (ML)^2 models avoids some of the problems that traditionally plague formal specification languages

A Language to Formalize and to Operationalize Problem-solving Strategies of Structured Knowledge Models

This paper describes a proposal of a language called Link which has been designed to formalize and operationalize problem solving strategies. This language is used within a software environment called KSM (Knowledge Structure Manager) which helps developers in formulating and operationalizing structured knowledge models. The paper presents both its syntax and dynamics, and gives examples of well-known problem-solving strategies of reasoning formulated using this language

Structural representations of unstructured knowledge, Journal of Telecommunications and Information Technology, 2005, nr 3

Knowledge should be represented in a formal, structured manner if we want to process and manage it. Unfortunately a source knowledge presented in many documents has informal, unstructured shape. The goal of these considerations is to present the methods of translation from the textual, unstructured knowledge to the structured knowledge, preserving textual form

Cokace: A Centaur-based environment for CommonKADS Conceptual Modelling Language

International audienceIn order to help the knowledge engineer during knowledge modelling phase, we developed an environment for the conceptual modelling language CML offered by the CommonKADS methodology. This environment, called Cokace, was designed using Centaur, a programming environment generator, that was usually exploited for building environments dedicated to software engineering languages. Thanks to Centaur, Cokace provides the knowledge engineer with structured edition, static validation and dynamic interpretation of CML expertise models. Cokace allows the knowledge engineer to simulate a reasoning on CML expertise models, and enables verification and evaluation of such expertise models before implementation of the final knowledge-based system. This work illustrates an example of the benefits knowledge engineering can get from well established techniques and tools available in software engineering

Structure preserving specification languages for knowledge-based systems

Much of the work on validation and verification of knowledge based systems (KBSs) has been done in terms of implementation languages (mostly rule-based languages). Recent papers have argued that it is advantageous to do validation and verification in terms of a more abstract and formal specification of the system. However, constructing such formal specifications is a difficult task. This paper proposes the use of formal specification languages for KBS-development that are closely based on the structure of informal knowledge-models. The use of such formal languages has as advantages that (i) we can give strong support for the construction of a formal specification, namely on the basis of the informal description of the system; and (ii) we can use the structural correspondence to verify that the formal specification does indeed capture the informally stated requirements

Knowledge-Level Reflection

This paper presents an overview of the REFLECT project. It defines the notion of knowledge level reflection that has been central to the project, it compares this notion with existing approaches to reflection in related fields, and investigates some of the consequences of the concept of knowledge level reflection: what is a general architecture for knowledge level reflection, how to model the object component in such an architecture, what is the nature of reflective theories, how can we design such architectures, and what are the results of our actual experiments with such systems

A comparison of languages which operationalise and formalise {KADS} models of expertise

In the field of Knowledge Engineering, dissatisfaction with the rapid-prototyping approach has led to a number of more principled methodologies for the construction of knowledge-based systems. Instead of immediately implementing the gathered and interpreted knowledge in a given implementation formalism according to the rapid-prototyping approach, many such methodologies centre around the notion of a conceptual model: an abstract, implementation independent description of the relevant problem solving expertise. A conceptual model should describe the task which is solved by the system and the knowledge which is required by it. Although such conceptual models have often been formulated in an informal way, recent years have seen the advent of formal and operational languages to describe such conceptual models more precisely, and operationally as a means for model evaluation. In this paper, we study a number of such formal and operational languages for specifying conceptual models. In order to enable a meaningful comparison of such languages, we focus on languages which are all aimed at the same underlying conceptual model, namely that from the KADS method for building KBS. We describe eight formal languages for KADS models of expertise, and compare these languages with respect to their modelling primitives, their semantics, their implementations and their applications. Future research issues in the area of formal and operational specification languages for KBS are identified as the result of studying these languages. The paper also contains an extensive bibliography of research in this area

Using CommonKADS to Build an Expertise Model for Breast Cancer Prognosis and Therapy

One of the major aspects in breast cancer research is the identification of prognostic factors accurate enough to define different therapeutic decisions; each prognostic factor on its own is not sufficient for the prediction of the biological behaviour of the tumour, but a combination of these parameters is necessary. More over, nowadays growth of cancer literature, specifically on biological aspects, is of exponential nature, and the management of the knowledge deriving from cancer research needs a knowledge conceptualisation in order to semplify the process of guideline production in cancer prognosis and therapy. The work described here focuses on the definition of a conceptual knowledge model of the prognosis and the therapy of breast cancer. Our approach to the conceptualisation of the problem fol lows the CommonKADS (Knowledge Acquisition and Design Structuring) Library for Expertise Modelling. The aim of this work is to provide a first conceptual- isation of breast cancer prognosis and therapy, while evaluating the efficacy of the Com monKADS methodology in facing the problem