Design and construction of maintainable knowledge bases through effective use of entity-relationship modeling techniques

The use of an accepted logical database design tool, Entity-Relationship Diagrams (E-RD), is explored as a method by which conceptual and pseudo-conceptual knowledge bases may be designed. Extensions to Peter Chen\u27s classic E-RD method which can model knowledge structure used by knowledge-based applications are explored. The use of E-RDs to design knowledge bases is proposed as a two-stage process. In the first stage, and E-RD, termed the Essential E-RD, is developed of the realm of the problem or enterprise being modeled. The Essential E-RD is completely independent of any knowledge representation model (KRM) and is intended for the understanding of the underlying conceptual entities and relationships in the domain of interest. The second stage of the proposed design process consists of expanding the Essential E-RD. The resulting E-RD, termed the Implementation E-RD, is a network of E-RD-modeled KRM constructs and will provide a method by which the proper KRM may be chosen and the knowledge base may be maintained. In some cases, the constructs of the Implementation E-RD may be mapped directly to a physical knowledge base. Using the proposed design tool will aid in both the development of the knowledge base and its maintenance. The need for building maintainable knowledge bases and problems often encountered during knowledge base construction will be explored. A case study is presented in which this tool is used to design a knowledge base. Problems avoided by the use of this method are highlighted, as are advantages the method presents to the maintenance of the knowledge base. Finally, a critique of the ramifications of this research is presented, as well as needs for future research

A study of the methodologies currently available for the maintenance of the knowledge-base in an expert system

This research studies currently available maintenance methodologies for expert system knowledge bases and taxonomically classifies them according to the functions they perform. The classification falls into two broad categories. These are: (1) Methodologies for building a more maintainable expert system knowledge base. This section covers techniques applicable to the development phases. Software engineering approaches as well as other approaches are discussed. (2) Methodologies for maintaining an existing knowledge base. This section is concerned with the continued maintenance of an existing knowledge base. It is divided into three subsections. The first subsection discusses tools and techniques which aid the understanding of a knowledge base. The second looks at tools which facilitate the actual modification of the knowledge base, while the last secttion examines tools used for the verification or validation of the knowledge base. Every main methodology or tool selected for this study is analysed according to the function it was designed to perform (or its objective); the concept or principles behind the tool or methodology: and its implementation details. This is followed by a general comment at the end of the analysis. Although expert systems as a rule contain significant amount of information related to the user interface, database interface, integration with conventional software for numerical calculations, integration with other knowledge bases through black boarding systems or network interactions, this research is confined to the maintenance of the knowledge base only and does not address the maintenance of these interfaces. Also not included in this thesis are Truth Maintenance Systems. While a Truth Maintenance System (TMS) automatically updates a knowledge base during execution time, these update operations are not considered \u27maintenance\u27 in the sense as used in this thesis. Maintenance in the context of this thesis refers to perfective, adaptive, and corrective maintenance (see introduction to chapter 4). TMS on the other hand refers to a collection of techniques for doing belief revision (Martin, 1990) . That is, a TMS maintains a set of beliefs or facts in the knowledge base to ensure that they remain consistent during execution time. From this perspective, TMS is not regarded as a knowledge base maintenance tool for the purpose of this study

Data degradation to enhance privacy for the Ambient Intelligence

Increasing research in ubiquitous computing techniques towards the development of an Ambient Intelligence raises issues regarding privacy. To gain the required data needed to enable application in this Ambient Intelligence to offer smart services to users, sensors will monitor users' behavior to fill personal context histories. Those context histories will be stored on database/information systems which we consider as honest: they can be trusted now, but might be subject to attacks in the future. Making this assumption implies that protecting context histories by means of access control might be not enough. To reduce the impact of possible attacks, we propose to use limited retention techniques. In our approach, we present applications a degraded set of data with a retention delay attached to it which matches both application requirements and users privacy wishes. Data degradation can be twofold: the accuracy of context data can be lowered such that the less privacy sensitive parts are retained, and context data can be transformed such that only particular abilities for application remain available. Retention periods can be specified to trigger irreversible removal of the context data from the system

An ontology framework for developing platform-independent knowledge-based engineering systems in the aerospace industry

This paper presents the development of a novel knowledge-based engineering (KBE) framework for implementing platform-independent knowledge-enabled product design systems within the aerospace industry. The aim of the KBE framework is to strengthen the structure, reuse and portability of knowledge consumed within KBE systems in view of supporting the cost-effective and long-term preservation of knowledge within such systems. The proposed KBE framework uses an ontology-based approach for semantic knowledge management and adopts a model-driven architecture style from the software engineering discipline. Its phases are mainly (1) Capture knowledge required for KBE system; (2) Ontology model construct of KBE system; (3) Platform-independent model (PIM) technology selection and implementation and (4) Integration of PIM KBE knowledge with computer-aided design system. A rigorous methodology is employed which is comprised of five qualitative phases namely, requirement analysis for the KBE framework, identifying software and ontological engineering elements, integration of both elements, proof of concept prototype demonstrator and finally experts validation. A case study investigating four primitive three-dimensional geometry shapes is used to quantify the applicability of the KBE framework in the aerospace industry. Additionally, experts within the aerospace and software engineering sector validated the strengths/benefits and limitations of the KBE framework. The major benefits of the developed approach are in the reduction of man-hours required for developing KBE systems within the aerospace industry and the maintainability and abstraction of the knowledge required for developing KBE systems. This approach strengthens knowledge reuse and eliminates platform-specific approaches to developing KBE systems ensuring the preservation of KBE knowledge for the long term

The changing perception in the artefacts used in the design practice through BIM adoption

When CAD (Computer Aided Design) was generally adopted in the early 1990’s, the hand drawn process was replaced with the CAD drawing but the nature of the artefacts / deliverables and the exchanges of information between disciplines remained fundamentally the same. The deliverables remained 2D representations of 3D forms and Specifications and Bill of Quantities. However, the building industry is under great pressure to provide value for money, sustainable design and construction. This has propelled the adoption of Building Information Modelling (BIM). BIM is a foundational tool for a team based lean design approach. It can enable the intelligent interrogation of design; provide a quicker and cheaper design production; better co-ordination of documentation; more effective change control; less repetition of processes; a better quality constructed product; and improved communication both for the architectural practice and across the supply chain. As BIM enables a new of working methodology, it entails the change in perceiving artefacts used and deliverables produced in the design and construction stages. In other words, defining what the informational issues are, who does what and who is responsible for what and the level of detail required at each stage in design and construction is critically important to adopt and implement BIM in the construction sector. This paper presents the key findings through the action research methodology about the change in the nature of artefacts and deliverables resulting from the BIM adoption in the KTP (Knowledge Transfer Partnership) project undertaken by the University of Salford and John McCall Architects

Knowledge-based reasoning in the Paladin tactical decision generation system

A real-time tactical decision generation system for air combat engagements, Paladin, has been developed. A pilot's job in air combat includes tasks that are largely symbolic. These symbolic tasks are generally performed through the application of experience and training (i.e. knowledge) gathered over years of flying a fighter aircraft. Two such tasks, situation assessment and throttle control, are identified and broken out in Paladin to be handled by specialized knowledge based systems. Knowledge pertaining to these tasks is encoded into rule-bases to provide the foundation for decisions. Paladin uses a custom built inference engine and a partitioned rule-base structure to give these symbolic results in real-time. This paper provides an overview of knowledge-based reasoning systems as a subset of rule-based systems. The knowledge used by Paladin in generating results as well as the system design for real-time execution is discussed