Knowledge Acquisition Tool Research Articles

Knowledge acquisition assisted by CLIPS programming

Knowledge acquisition is important and machine learning techniques can be used to achieve automated knowledge acquisition. This article examines how knowledge acquisition can be assisted by programming using CLIPS (an acronym for C Language Integrated Production System). A machine learning preprocessor has been developed for the CLIPS environment, so that the CLIPS rule-base can be expanded by adding rules generated through machine learning techniques. The paper also shows how knowledge updating can be supported in the CLIPS environment itself. Operational engineering knowledge is captured in a data structure called a decision tree, and its structure can be updated when new knowledge is acquired. In addition, some advanced features are also briefly discussed, including using COOL (the CLIPS object-oriented language) for knowledge acquisition in a software product recommendation system, as well as the design of a self-evolving knowledge-acquisition tool.

An interactive tool for knowledge base refinement

We present a tool that combines two main trends of knowledge base refinement. The first is the construction of interactive knowledge acquisition tools and the second is the development of machine learning methods that automate this procedure. The tool presented here is interactive and gives experts the ability to evaluate an expert system and provide their own diagnoses on specific problems, when the expert system behaves erroneously. We also present a database scheme that supports the collection of specific instances. The second aspect of the tool is that knowledge base refinement and machine learning methods can be applied to the database, in order to automate the procedure refining the knowledge base. In this paper we examine the application of inductive learning algorithms within the proposed framework. Our main goal is to encourage the experts to evaluate expert systems and to introduce new knowledge, based on their experience.

A strategy for developing practice guidelines for the ICU using automated knowledge acquisition techniques.

To implement practice guideline entry tools in a reminder system in order to provide decision support to health care workers in clinical care and emergency care environments. To design a knowledge acquisition environment that enables physicians to formulate, update, and verify guidelines without the assistance of a knowledge engineer. We developed a knowledge acquisition environment for the Intensive Care Unit (ICU) consisting of 1) a graphical knowledge acquisition tool, 2) tools that perform logical and semantic tests on proposed guidelines, 3) a Patient Data Management System (PDMS) containing clinical patient data, and 4) an expert system that reminds ICU health care workers of inconsistencies between a treatment plan and implemented guidelines. Physicians enter the guidelines using the knowledge acquisition tool, after which consistency and correctness tests are performed on the guidelines. The guidelines are then transferred to the knowledge base of the reminder system and validated by applying the new guidelines to a large stored data set of previous patients. If the new guidelines are approved, they are exported to the reminder system that is used in daily practice. ICU physicians used the knowledge acquisition tool to enter 58 guidelines into the reminder system's knowledge base. These guidelines were tested on a data set consisting of 803 previously admitted patients. As a result, 27 guidelines fired at least once, generating 406 reminders in total. Of the 406 generated reminders, 356 (88%) were issued correctly and 50 (12%) were false alarms. The reminders that were issued correctly involved 3 situations: 1) the database contained inconsistent or incomplete information, 2) the actions or decisions of the health care workers were not the most appropriate ones, and 3) there was a potential risk involved. All false alarms were caused by the fact that the corresponding guidelines were not specific enough to handle certain exceptions. As a result of this analysis, the guidelines could be improved in such a way as to eliminate all false alarms. These first results demonstrate that this bottom-up knowledge acquisition strategy, implemented by the automated knowledge acquisition tools, enables medical specialists to improve the quality of computer support in an ICU without assistance of a knowledge engineer.

The DELPHI method as a consensus and knowledge acquisition tool for the evaluation of the DIABETES system for insulin administration.

DIABETES is a decision-support system in the field of insulin administration. System performance evaluation is particularly difficult because of the absence of a uniform decision-making model followed by the specialists. The DELPHI method has been selected since it is appropriate for those domains where there is divergence among experts' opinions. The DELPHI approach helps a number of diabetologists arrive at a consensus and thus it facilitates performance evaluation and further knowledge acquisition. Insulin administration regimes, for 100 diabetic subjects, were proposed by DIABETES and five diabetologists (round 1). These suggestions were compiled and forwarded back to the specialists who proposed a second management approach (round 2). In each case, the experts were asked to justify their decision and comment on the suggestions of their colleagues and DIABETES. A novel scoring system for quantification of agreement was adopted. The DELPHI procedure significantly increased the agreement among the diabetologists from 67% to 84% (X2, p = 0.0001). The agreement between experts' and DIABETES recommendations was to a level of 54%. A total of 3500 comments were acquired by the experts.

On random sets, knowledge acquisition, and pattern recognition

In this article, we investigate knowledge acquisition and pattern recognition by using the principles of random sets. Based on random set theory, we develop some estimation theorems and procedures for set-valued statistics, such as nonparametric estimators and set-valuedization techniques. Under random interval assumption, we establish some special possibility distributions that can be easily implemented in knowledge acquisition tools. The knowledge studied here are rules describing relationships between various concepts, as used in diagnosis (pattern recognition) expert systems. Several examples are given to illustrate the estimation theorems and procedures for the acquisition of concepts and relationships. We use our acquisition techniques on a modeling prediction example in two different ways: One is by acquiring the concepts and relationships simultaneously; another is by acquiring rules for predefined concepts. On two classification problems, we use our methods to acquire classification rules. The results are compared with several machine learning methods. © 1996 John Wiley & Sons, Inc.

Knowledge acquisition by random sets

In this article we investigate knowledge acquisition (KA) and its relationships to random sets. Based on random set theory, we develop some estimation theorems and procedures for set-valued statistics such as nonparametric estimators. Under random interval assumption, we establish some special possibility distributions that can be easily implemented in KA tools. The knowledge studied here are rules describing relationships between various concepts, as used in diagnosis (pattern recognition) expert systems. © 1996 John Wiley & Sons, Inc.

Knowledge acquisition and knowledge representation with class: the object-oriented paradigm

Knowledge acquisition and knowledge representation are the fundamental building blocks of knowledge-based systems (KBSs). How to efficiently elicit knowledge from experts and transform this elicited knowledge into a machine usable format is a significant and time consuming problem for KBS developers. Object-orientation provides several solutions to persistent knowledge acquisition and knowledge representation problems including transportability, knowledge reuse, and knowledge growth. An automated graphical knowledge acquisition tool is presented, based upon object-oriented principles. The object-oriented graphical interface provides a modeling platform that is easily understood by experts and knowledge engineers. The object-oriented base for the automated KA tool provides a representation independent methodology that can easily be mapped into any other object-oriented expert system or other object-oriented intelligent tools.

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.

EECB: a knowledge elicitation tool based on repertory grid and city block metric

There are several knowledge acquisition tools that use repertory grids as a technique to elicitate knowledge. One of the steps involved in this technique is the debugging of the grid using procedures based on cluster analysis. Most of these tools employ a Euclidean metric for the clustering. However, some authors find evidence that people use the city block metric when rating similarities. In this paper, the tool ComPar ( Comparación de Pares), developed to acquire data in a knowledge engineering experiment, is described. This tool tries to determine what kind of metric is the most often used by people in similarity judgments. After the experiment, it was concluded that there is evidence that individuals use mainly a city block metric when trying to judge similarity between different things. Accordingly, a tool supporting the repertory grid technique called EECB ( emparrillado euclı́deo, “city block” in Spanish) was developed afterwards, allowing the possibility of distance calculations using city block metric.

Knowledge sharing and reuse for engineering design integration

Completion of complex engineering designs (e.g. the design of offshore oil platforms, ships, etc.) often involves a number of agents who have to share or reuse design models within a distributed environment. This article presents a knowledge sharing workbench which enables agents to share common domain knowledge, based on the problems which emerge in the design process. The workbench includes Application Programming Interfaces provided by expert system shells, an Object Request Broker, and a number of ontologies to facilitate the construction of new knowledge-based systems. In addition, the distributed knowledge acquisition tools generated by the workbench can maintain model consistency between agents when specification changes occur in any of the agents. A case study from the petrochemical industry is used to illustrate the use of workbench for the integration of a number of agents. This case study demonstrates that a process flow knowledge model in an offshore petrochemical plant, designed for operational purposes (e.g. fault diagnosis), can be reused to form part of a new knowledge-based system which generates the data for plant layout design.

A Risk Evaluation System for Contaminated Sites

A computer system is presented which is an effective support for the Saxonian government offices responsible for the evaluation of contaminated sites. This system consists of three main parts: the knowledge based program XUMA, the evaluation program GEFA and an analysis plan program. XUMA includes a knowledge base with the principal methods for handling contaminated sites. The main feature of XUMA is a knowledge acquisition tool for the evaluation method and the basic knowledge for preparation of analysis plans. GEFA is a program for the systematic investigation and registration of sites suspected of being contaminated and for their evaluation with regard to environmental hazards. With the help of the analysis plan program the user is able to create a plan for the chemical-analytical investigations which are required to determine the potential hazard of substances in the site.

Knowledge discovering for document classification using tree matching in TEXPROS

This paper describes a knowledge-based system for classifying documents based upon the layout structure and conceptual information extracted from their contents. The spatial elements in a document are laid out in rectangular blocks which are represented by nodes in an ordered labeled tree, called the “Layout Structure Tree” (L-S Tree). Each leaf node of an L-S Tree points to its corresponding block content. A Knowledge Acquisition Tool (KAT) is devised to perform the inductive learning from L-S Trees of document samples, and then generate the Document Sample Tree and Document Type Tree bases. A testing document is classified if a Document Type Tree is discovered as a substructure of the L-S Tree of the testing document. Then we match the L-S Tree with the Document Sample Trees of the classified document type to find the format of the testing document. The Document Sample Trees and Document Type Trees are called Structural Knowledge Base (SKB). The tree discovering and matching processes involve comparing the SKB trees and a testing document's L-S Tree by using pattern matching and discovering toolkits. Our experimental results demonstrate that many office documents can be classified correctly using the proposed approach.

Framework for a Geographic Districting DSS using an Intelligent Object-oriented Model

We describe a framework for a geographic districting DSS. The components included in the framework are an object-oriented geographic data model with built-in capabilities for storing spatial and non-spatial information pertaining to the geographic objects represented in the model; an intelligent spatial query processor that uses domain specific knowledge to reason over geographic data and process complex districting queries; and a knowledge acquisition tool that is used to capture domain specific knowledge. These components are combined together to create an integrated geographic districting DSS. We describe our data model, the rule processing subsystem in the model, and the knowledge acquisition subsystem in detail. Finally, we describe a pilot implementation based on the framework which uses real life census data and demonstrates how the system would work in practice

Using an unsupervized neural network and decision tree as knowledge acquisition tools for FMS scheduling

This paper applies neural network learning techniques and a decision tree method to obtain scheduling knowledge for flexible manufacturing systems. A search is made for the reverse relationship between the FMS system performance and its corresponding decision parameters. More specifically, the learning function is to acquire the scheduling knowledge which determines the decision-mix to control adequately the system in fulfilling a specified system performance. This paper first configures a typical flexible manufacturing system and defines a set of decision parameters along with a set of system performance evaluation criteria. The learning procedure initiates accumulating simulated data through the system. Next, the ART2 ( adaptive resonance theory) neural model takes the system performance data as inputs and forms performance classes according to their similarities. Finally, the decision tree construction method is applied to extract the definition of each class, The definitions are converted into rule-type knowledge associated with certainty factors, and used as the scheduling knowledge.

A REPERTORY GRID APPROACH FOR SOFTWARE COMPONENTS CLASSIFICATION RETRIEVAL

ABSTRACT Software reuse is an important approach to increase software quality and productivity. There are many factors that may affect the result of software reuse. Among them, software component classification and retrieval are two important factors. Identifying the major component attributes is a necessary condition for creating a high efficiency component classification and retrieval. In this paper, we define a component attribute set and its major operations to identify the relationships between two components. In order to increase the efficiency of software reuse, we apply the repertory grid technique which is a more popular skill in knowledge acquisition tools to the component classification and retrieval. According to the identified component relationships and the concept of the repertory grid technique, the component classification model can be created, the reasonable candidate component ranking approach can be presented, and an algorithm for selecting a set of components with combination potential and mutual complement capability can be provided.

A case-based consiliarius for therapy recommendation (ICONS): computer-based advice for calculated antibiotic therapy in intensive care medicine

We report here on the system ICONS which utilizes case-based reasoning for medical decision support. As an application domain we have chosen the medical field of ‘calculated antibiotic therapy’ in an intensive care medicine setting. The system ICONS which runs on a personal computer suggests adequate antibiotic therapy regimen satisfying medical and economic conditions. To speed up the process of finding an adequate antibiotic therapy for a current patient, case-based reasoning is used for finding previously documented similar cases and for modifying them according to the requirements of the current patient. To reduce the momory capacity for the documentation of cases, collections of similar cases are clustered to prototypes. Medical knowledge is represented within a hierarchy of such prototypes and cases and an additional context-sensitive background knowledge-base. A knowledge acquisition tool was programmed that allows revisions of the background medical knowledge-base by simple and comprehensive methods. In addition to the advantage of producing site-specific and time-dependent knowledge, case-based reasoning is a practical method for speeding-up the process of generating and evaluating hypotheses in medical classification tasks.

Concept Formation in WWTP by Means of Classification Techniques: ACompared Study

Although activated sludge process is a very widely used biological process in wastewater treatment plants (WWTP), and there are properly functioning control loops such as that of dissolved oxygen, in practice, this type of plant requires a major time investment on the part of the operator, involving many manual operations. Treatment plants work well most of the time, as long as there are not unforeseen occurrences. Normal operating situations (generally similar to design conditions) can be treated mathematically by using efficient control algorithms. However, there are situations in which the control system cannot properly manage the plant, and in which the process can only be efficiently managed thanks to the operator‘s experience. This is a case in which a knowledge-based system may be useful. One of the difficulties inherent to the development of a knowledge-based system is to obtain the knowledge base (i.e., {\it knowledge\ acquisition}), specially when dealing with a wide, complicated and {\it ill-structured} field. Among the aims of this work are those to show how semi-automatic knowledge acquisition tools could help human experts to organize their knowledge about their domain and also, to compare the power of different approaches of {\it knowledge\ acquisition} to the same database. In this paper are presented the results obtained from applying two different classification techniques to the development of knowledge-bases for the management of an activated sludge process.

Some Techniques of Reducing the Dangers of Combinatorial Explosion in Automatic Knowledge Acquisition

The danger of getting into a combinatorial explosion has remained probably the most serious impediment to using knowledge-based systems for real-life problems. Justifiable heuristics must cut down the size of potentially huge search spaces to a realistic and manageable size - without jeopardizing the success of finding satisfactory solutions. This paper describes some techniques that have proved to be effective in the operation of two different systems: the Quasi-Optimizer (QO) and the Generalized Production Rule System (GPRS). The QO is a domain-independent automatic knowledge acquisition tool that can obtain, verify, fuse and optimize human expertise. It generates computer models, descriptive theories, of human decision making strategies, and can also select and combine the best components of several such models into a single strategy which may be considered a normative theory in the statistical sense. The techniques developed to reduce the danger of combinatorial explosion with the QO include selecting the most independent (near-orthogonal) decision variables, chunking conceptually coherent decision variables, eliminating statistical outlier values, and using dynamically evolving experimental designs to result in a near-uniform reliability distribution of the strategy responses. The GPRS can estimate/predict values of hidden variables and can thus serve as a module of an expert system in need of numerical or functional estimates of hidden variable values. (Hidden variables can be observed and measured only intermittently and at irregular points of time and space - in contrast with open variables whose values can be identified at any time and location.) The estimation is based on generalized production rules expressing stochastic and potentially causal relations between known values of hidden variables and certain mathematical properties of the open variable distribution. A multi-dimensional learning process adds to, consolidates and optimizes the generalized rule base. It gradually merges “similar enough” production rules, and eliminates spurious and statistically not justifiable ones. Such processes reduce the danger of combinatorial explosions in knowledge acquisition. Finally, we note that human decision makers appear to resort to similar methods, although in a less systematic manner, when they are confronted with very large decision spaces.

Managing context in enterprise knowledge processes

Because competitive advantage accrues to those enterprises that effectively manage their ‘knowledge’, researchers are seeking a viable organizational epistemology. This paper addresses one epistemological challenge - the slippery but critical notion of ‘context’ - by presenting and substantiating four claims. First, as ‘that which imparts meaning’, context is inherently important to epistemological enquiry generally and enterprise knowledge processes specifically. Second, context is a key issue in three areas of current enterprise (re-) engineering and ‘organizational learning’ research: (a) systemic theories; (b) knowledge acquisition tools; and (c) conversation management. Third, a systemic perspective requires redefining context as a process ( contexture) embedded in a system's intrinsic operational ‘situatedness’. Finally, this shift of perspective can be practically implemented through innovative enterprise knowledge acquisition procedures. One such innovative procedure ( nichepicking) is illustrated by a case study.

Concepts and tools for lexical knowledge acquisition

Concepts and tools for lexical knowledge acquisition