GDKAT: A goal-driven knowledge acquisition tool for knowledge base development

While knowledge-based systems are being used extensively to assist in making decisions, a critical factor that affects their performance and reliability is the quantity and quality of the knowledge bases. Knowledge acquisition requires the design and development of an in-depth comprehension of knowledge modeling and of applicable domain. Many knowledge acquisition tools have been developed to support knowledge base development. However, a weakness that is revealed in these tools is the domain-dependent and complex acquisition process. Domain dependence limits the applicable areas and the complex acquisition process makes the tool difficult to use. In this paper, we present a goal-driven knowledge acquisition tool (GDKAT) that helps elicit and store experts' declarative and procedural knowledge in knowledge bases for a user-defined domain. The designed tool is implemented using the object-oriented design methodology under C++ Windows environment. An example that is used to demonstrate the GDKAT is also delineated. While the application domain for the example presented is reflow soldering in surface mount printed circuit board assembly, the GDKAT can be used to develop knowledge bases for other domains also.     

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.     

Automating knowledge acquisition: a propositional approach torepresenting expertise as an alternative to repertory gridtechnique

Repertory grid technique plays a central role in the elicitation methodology of many well-reported knowledge acquisition tools or workbenches. However, the dependability of these systems is low where the technique breaks down or proves inadequate due to limited expressive power and other problems. The paper introduces an alternate approach based on Personal Construct Theory that elicits an expert's knowledge as a network of terms that constitutes a propositional formalism. An extended example is used to both highlight the difficulties encountered using repertory grids and illustrate how these are overcome using the proposed approach. The results of an empirical study are presented where an experienced clinician compared the knowledge structures that she constructed for a diagnostic task using each elicitation technique. Furthermore, although the network representation is amenable to inductive learning methods for generating production rules, an inference method is demonstrated which reveals the formalism's categorical reasoning potential. The authors conclude that it is more appropriate to classify such methods as either mediating or immediate rather than the knowledge structures they employ. The paper contributes to a better understanding of constructivist formalisms developed for knowledge acquisition     

Windows Mobile LiveSD Forensics

More and more often, smartphones are relevant targets of civil and criminal investigations. Currently, there are several tools available to acquire forensic evidence from smartphones. Unfortunately, most of these tools require to connect the smartphone under investigation through a cable to an external device, like a computer or a multimeter. Some tools even require to disassemble the chips from the smartphone board.In this paper, we propose LiveSD Forensics, an on-device live data acquisition solution, to acquire evidence from both the Random-Access Memory (RAM) and the Electronically Erasable Programmable Read Only Memory (EEPROM) of Windows Mobile Devices.To the best of our knowledge, LiveSD Forensics is the only tool that performs on-device live data acquisition of the RAM and the EEPROM of Windows Mobile Devices. LiveSD Forensics uses a standard SD-Card equipped with tailored code to perform the data acquisition. Compared to other existing tools, LiveSD also generates the smallest memory alteration. Finally, to assess the effectiveness of the proposed methodology, we test LiveSD in a practical scenario, that is retrieving from the RAM the cryptographic key used by a known on-the-fly encryption tool. Results support the quality and effectiveness of our proposal.     

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.     

A tool for task-based knowledge and specification acquisition

Knowledge acquisition has been identified as the bottleneck for knowledge engineering. One of the reasons is the lack of an integrated methodology that is able to provide tools and guidelines for the elicitation of knowledge as well as the verification and validation of the system developed. Even though methods that address this issue have been proposed, they only loosely relate knowledge acquisition to the remaining part of the software development life cycle. to alleviate this problem, we have developed a framework in which knowledge acquisition is integrated with system specifications to facilitate the verification, validation, and testing of the prototypes as well as the final implementation. to support the framework, we have developed a knowledge acquisition tool, TAME. It provides an integrated environment to acquire and generate specifications about the functionality and behavior of the target system, and the representation of the domain knowledge and domain heuristics. the tool and the framework, together, can thus enhance the verification, validation, and the maintenance of expert systems through their life cycles. © 1994 John Wiley & Sons, Inc.     

Knowledge conceptualization tool

Knowledge acquisition is one of the most important and problematic aspects of developing knowledge-based systems. Many automated tools have been introduced in the past, however, manual techniques are still heavily used. Interviewing is one of the most commonly used manual techniques for a knowledge acquisition process, and few automated support tools exist to help knowledge engineers enhance their performance. The paper presents a knowledge conceptualization tool (KCT) in which the knowledge engineer can effectively retrieve, structure, and formalize knowledge components, so that the resulting knowledge base is accurate and complete. The KCT uses information retrieval technique to facilitate conceptualization, which is one of the human intensive activities of knowledge acquisition. Two information retrieval techniques employing best-match strategies are used: vector space model and probabilistic ranking principle model. A prototype of the KCT was implemented to demonstrate the concept. The results from KCT are compared with the outputs from a manual knowledge acquisition process in terms of amount of information retrieved and the process time spent. An analysis of the results shows that the process time to retrieve knowledge components (e.g., facts, rules, protocols, and uncertainty) of KCT is about half that of the manual process, and the number of knowledge components retrieved from knowledge acquisition activities is four times more than that retrieved through a manual process     

Knowledge for courseware engineering: A framework for inductive knowledge acquisition based upon evaluation of adaptable courseware products

Authors or adaptors of courseware products preferably should receive support in the process of development and adaptation of courseware products. A predictive agent is defined as a system that is able to predict the expected effectiveness of various composable products from current product attributes. The described research addresses the questions of how to acquire the necessary knowledge for a predictive agent, how to organize this knowledge, and how to link it with methods and tools for courseware authoring and adaptation. We propose to use a methodology, derived from the field of machine learning, and present a framework for applying inductive knowledge acquisition based upon the empirical evaluation of adaptable courseware products.     

Medical knowledge management for specific hospital departments

Medical knowledge representation and management is concerned with how to organise the often vague clinical experience of medical staff required for computable models. However, few knowledge management and acquisition tools have entered routine use, since such tools are not perceived by physicians as part of the clinical information process. An attempt to partially solve this problem, we identify two key aspects of knowledge representation and management tasks. The first is to adopt a medical knowledge standardisation to provide a consistent terminology control and to simplify the integration between knowledge management tools and the health information system. The second is to establish an effective knowledge acquisition process in specific medical fields by adapting knowledge acquisition tools. Therefore, the main goal of this work is to define computational models and to design mechanisms for the effective acquisition and management of medical knowledge in real-life hospital departments. To this end, we analyse the representation of medical knowledge (based on deep-causal models) and the development of knowledge management tools (based on ontologies), integrated within the information processing activities of the clinical user. Finally, we illustrate its applicability in the Intensive Care Unit and Pediatry scenarios.     

Knowledge acquisition and integration tools aiming to support managerial planning in Greek SMEs

Most existing expert systems are defined in structured task domains. However, many real-life decision tasks are novel, unstructured and consequential. To support these tasks, expert systems are needed which provide an integrated environment capable of capturing new knowledge by updating the existing knowledge base. This paper describes the incremental development process of an expert system, from the initial gathering of data up to the development of knowledge acquisition tools and knowledge integration methodologies. The expert system developed addresses managerial planning tasks of Greek small-to-medium sized enterprises (SMEs). The manager sets values for parameters specifying environmental and company characteristics. The expert system responds with suggestions on feasible tactics, objectives and strategies. To cope with the changes of planning situations and also to improve the integrity of the knowledge base as the manager gains experience, knowledge acquisition tools have been introduced. These knowledge acquisition tools, which are manipulated directly by the manager, provide the system with additional knowledge and validate the knowledge already embedded in the knowledge base.     

Eliciting knowledge and transferring it effectively to aknowledge-based system

The knowledge acquisition bottleneck impeding the development of expert systems is being alleviated by the development of computer-based knowledge acquisition tools. These work directly with experts to elicit knowledge, and structure it appropriately to operate as a decision support tool within an expert system. However, the elicitation of expert knowledge and its effective transfer to a useful knowledge-based system is complex and involves diverse activities. The complete development of a decision support system using knowledge acquisition tools is illustrated. The example is simple enough to be completely analyzed but exhibits enough real-world characteristics to give significant insights into the processes and problems of knowledge engineering     

Integrated knowledge acquisition architectures

An architecture for knowledge acquisition systems is proposed based upon the integration of existing methodologies, techniques and tools which have been developed within the knowledge acquisition, machine learning, expert systems, hypermedia and knowledge representation research communities. Existing tools are analyzed within a common framework to show that their integration can be achieved in a natural and principled fashion. A system design is synthesized from what already exists, putting a diversity of well-founded and widely used approaches to knowledge acquisition within an integrative framework. The design is intended to be clean and simple, easy to understand, and easy to implement. A detailed architecture for integrated knowledge acquisition systems is proposed that also derives from parallel cognitive and theoretical studies.     

农业智能系统的知识获取技术研究

作者及其团队长期针对农业领域的知识获取技术进行了系列性研究.阐述了运用智能引导、机器学习、数据挖掘、智能计算等技术的人工和自动/半自动的知识获取方法.这些方法能够有效地获取领域知识,发现隐含模式,进行知识精化.研发了知识获取工具.这些方法和工具反映了知识获取技术对农业信息工程所起的重要作用.     

Parallel Automated Knowledge Acquisition of Case-Based Semantic Networks from Relational Databases

The number of databases that are accessible over networks within organizations is increasing. This paper presents a methodology for automatically converting the data in these databases into a useful knowledge base of case-based semantic networks that can be accessed through a browsing facility. A parallel processing strategy has been implemented for this knowledge acquisition process to support its scalability to large databases. This methodology has potential application in the development of organizational intranets. It can also be used for retrospective browsing of the context of interesting patterns discovered by data mining. The database examples used in this paper are from clinical laboratories that provide data to a hospital infection control committee. Even though the results presented here use a single domain, the methodology can be used with no changes to explore the construction of multidomain knowledge bases.     

Development of the knowledge base of an expert system for civil rights investigation

Abstract: A methodology that was successfully applied to the development of the knowledge base of a legal expert system is presented. The methodology consists of five distinct phases: model building, knowledge acquisition, knowledge integration, partitioning and external design. The phases are described in detail and illustrated with examples from the application area, which is civil rights complaint investigation.     

Lessons learned from developing a mission-critical expert system with multiple experts through rapid prototyping

The expert systems technology is ubiquitous among the mission-critical tasks in the defense area. Knowledge compiled through the military applications can serve as a remarkable source of learning opportunities for those in the civilian sector. In this paper, we describe the process of developing one such mission-critical military application, which contains an embedded expert system. A large number of experts simultaneously participated in numerous knowledge acquisition sessions to provide their expertise in air combat planning. The rapid prototyping methodology was employed as a vehicle to facilitate the knowledge acquisition process. By recounting the development process, we intend to share the lessons learned from this experience. These lessons are presented from three different perspectives, i.e. the knowledge engineers, the experts, and regarding the rapid prototyping methodology.     

Application of MOKA methodology in generative model creation using CATIA

The paper presents the method of generative model creation in CATIA system. The method covers not only the very process of model creation in CATIA system but also the whole complexity of the process, beginning with knowledge identification and acquisition and finally with explanation system creation that facilitates and justifies the usage of generative model itself. The method, in one of its parts, uses MOKA methodology and in particular its informal knowledge model and knowledge representation for generative model, which is special for CATIA.     

A product lifecycle management methodology for supporting knowledge reuse in the consumer packaged goods domain

The present globalized market is forcing many companies to invest in new strategies and tools for supporting knowledge management. This aspect is becoming a key factor in the industrial competitiveness for the presence of extended enterprises that normally deal with huge data exchange and share processes. This scenario is due to the presence of partners geographically distributed over the entire globe, that participate in different steps of the product lifecycle (product development, maintenance and recycling). At present, Product Lifecycle Management (PLM) seems to be the appropriate solution to support enterprises in this complex scenario, even though a real standardized approach for the implementation of knowledge sharing and management tools does not exist today. For this reason, the aim of this paper is to develop a knowledge management operative methodology able to support the formalization and the reuse of the enterprise expertise acquired while working on previous products. By focusing on consumer packaged goods enterprises and on the concept development phase (which is one of the most knowledge intensive phases of the whole product lifecycle), this research work has developed a new systematic methodology to support knowledge codification and knowledge management operations. The new methodology integrates the Quality Function Deployment (QFD) and the Teoriya Resheniya Izobreatatelskikh Zadatch (TRIZ). Also, a case study on the problem of waste disposal has been conducted to validate the proposed methodology.     

NEGATIVE AND POSITIVE EXPLANATIONS IN EXPERT SYSTEMS

A methodology for management of constraints in VLSI data bases is presented in this paper. The process of management is based on forma! mechanisms of attribute grammar theory. Examples of a modularized knowledge representation of a circuit at interconnection and logic levels based on attribute grammars illustrate the methodology. The ultimate goal and benefit of the proposed methodology is a well-defined development technique for VLSI design tools that will allow the user to generate a transformation system, a knowledge representation, and a system to manipulate this representation, all from a simple specification. In addition, context-dependent specifications at lower levels are automatically generated from the higher-level specifications.