Model-based Knowledge Acquisition Research Articles

Model-based knowledge acquisition in environmental decision support system for wastewater integrated management

The main goal of the Water Framework Directive is to achieve good chemical and ecological status of water bodies by 2015. The implementation of integrated river basin management, including sewer systems, wastewater treatment plants and receiving water bodies, is essential to accomplishing this objective. Integrated management is complex and therefore the implementation of control systems and the development of decision support systems are needed to facilitate the work of urban wastewater system (UWS) managers. Within this context, the objective of this paper is to apply integrated modelling of an UWS to simulate and analyse the behaviour of the 'Congost' UWS in Spain, and to optimize its performance against different types of perturbations. This analysis results in optimal operating set-points for each perturbation, improves river water quality, minimizes combined sewer overflows and optimizes flow lamination from storm water tanks. This is achieved by running Monte Carlo simulations and applying global sensitivity analysis. The set-points will become part of the knowledge base composed of a set of IF-THEN rules of the environmental decision support system being developed for this case study.

Using natural language sources in model-based knowledge acquisition

This paper introduces a well defined co-operation between domain expert, knowledge engineer, and knowledge acquisition and transformation tools. First, the domain expert supported by a hypertext tool generates an intermediate representation from parts of authentic texts of a domain. As a side effect, this representation serves as human readable documentation. In subsequent steps, this representation is semi-automatically transformed into a formal representation by knowledge acquisition tools. These tools are fully adapted to the expert's domain both in terminology and model structure which are developed by the knowledge engineer from a library of generic models and with preparation tools.

Causal domain model driven knowledge acquisition for expert diagnosis system development

Despite the successful operation of expert diagnosis systems in various areas of human activity these systems still show several drawbacks. Expert diagnosis systems infer system faults from observable symptoms. These systems usually are based on production rules which reflect so called ‘shallow knowledge’ of the problem domain. Though the explanation subsystem allows the program to explain its reasoning, deeper theoretical justifications of program's actions are usually needed. This may be one of the reasons why in recent years in knowledge engineering there has been a shift from rule-based systems to model-based systems. Model-based systems allow us to reason and to explain a system's physical structure, functions and behaviour, and thus, to achieve much better understanding of the system's operations, both in normal mode and under fault conditions. The domain knowledge captured in the knowledge base of the expert diagnosis system must include deep causal knowledge to ensure t he desired level of explanation. The objective of this paper is to develop a causal domain model driven approach to knowledge acquisition using an expert–acquisition system–knowledge base paradigm. The framework of structural modelling is used to execute systematic, partly formal model-based knowledge acquisition, the result of which is three structural models–one model of morphological structure and two kinds of models of functional structures. Hierarchy of frames are used for knowledge representation in topological knowledge base (TKB). A formal method to derive cause–consequence rules from the TKB is proposed. The set of cause–consequence rules reflects causal relationships between causes (faults) and sequences of consequences (changes of parameter values). The deep knowledge rule base consists of cause–consequence rules and provides better understanding of system's operation. This, in turn, gives the possibility to construct better explanation fa cilities for expert diagnosis system. The proposed method has been implemented in the automated structural modelling system ASMOS. The application areas of ASMOS are complex technical systems with physically heterogeneous elements.

Fault Diagnosis in an Expert System for Health Services Management in the Tropics

An integrated large-scale expert system called Health-2000, for the management of health services in regions where tropical diseases are endemic, has been designed. This system combines knowledge and databases, the contents of which are operated upon by an inference engine, to produce usable information. The system allows a host of applications, ranging from medical diagnosis to fault detection and preventive maintenance of biomedical equipment. The theoretical background and approach used in the development of the fault diagnosis and equipment maintenance sub-system of Health-2000 is presented. Model-based knowledge acquisition, and an extension of the Failure Modes, Effects and Criticality Analysis are two methodologies applied to build its knowledge bases. The inference engine which supports backward and forward chaining, operates on numerical and non-numerical facts, and uses fuzzy logic to handle vague and uncertain knowledge. Fault isolation proceeds in a top-down fashion, from equipment sub-system, to modules and components.

Causal model-based knowledge acquisition tools: discussion of experiments

The aim of this paper is to study causal knowledge and demonstrate how it can be used to support the knowledge acquisition process. The discussion is based on three experiments we have been involved in. First, we identify two classes of Causal Model-Based Knowledge Acquisition Tools (CMBKATs): bottom-up designed causal models and top-down designed causal models. We then go on to discuss the properties of each type of tool and how they contribute to the whole knowledge acquisition process.

Model-based knowledge acquisition for heuristic classification systems

We present a methodology and an implemented system, EAR*, for supporting knowledge acquisition. Its goal is to build an implementation-independent knowledge-level description of the target knowledge base, guided by our current understanding of the generic structure of heuristic classification systems.