Brian J. Garner

The role of artificial intelligence in knowledge management

Knowledge Management has emerged as a predominantly management discipline in the early to mid-90s. The American Productivity and Quality Centre has identified six common strategic elements [1] among US firms that have embraced this new field. Two elements are the formulation of business strategies and the appointment of Chief Knowledge Officers to better focus on the exploitation of core intellectual assets by business and Governments, specifically the need to capitalise on increasingly expensive human resources/process knowledge to achieve competitive advantage in global procurement (supply chain management), in product development, in customer relationship management (CRM) and in value-added services.

General purpose inference engine for canonical graph models

The design and implementation of a General Purpose Inference Engine for canonical graph models that is both flexible and efficient is addressed. Conventional inference techniques (e.g. forward chaining, backward chaining and mixed strategies) are described, and new modes of flexibility through the provision of inexact matching between data and assertions/rules are explained. In GPIE, scanning/searching of the rules in the rule base is restricted to a minimum during execution, but at the expense of compilation of the rule set prior to execution. The generality of the rule set is transparent to the inference engine, thereby permitting reasoning at various levels. This research demonstrates that a graph-based inference engine offering flexible control structures and inxact matching can complement intermediate notations, such as conceptual graphs, offering the expressive power of a rich knowledge representation formalism. The availability of an extendible graph processor for building appropriate canonical graph models presents the exciting prospect of a general purpose reasoning engine.

Context management in modeling information systems (IS)

Abstract Knowledge engineering methods are shown to have an important role in addressing the challenge of building trusted contexts; namely, in providing solutions to the problems of complexity, conformity, changeability and invisibility. Current information systems (IS) design practices, however, are seen to solve only those problems pertaining to the global enterprise system, while leaving the validation issues related to user/local models unresolved. Context management mechanisms associated with the user/local models are shown to provide a basis for dynamic validation of user requirements. The proposed context management process has been developed to allow context representation and dynamic knowledge structuring at the epistemological level.

Experimentation and self learning in continuous database marketing

We present a method for continuous database marketing that identifies target customers for a number of marketing offers using predictive models. The algorithm then selects the appropriate offer for the customer. Experimental design principles are encapsulated to capture more information that will be used to monitor and refine the predictive models. The updated predictive models are then used for the next round of marketing offers.

An Extendible Graph Processor For Knowledge Engineering

The theory of conceptual graphs offers a uniform and powerful knowledge representation formalism, and an extendible graph processor has been implemented to process domain dependent knowledge that is encoded in canonical graphs. Functional components in the extendible graph processor are described. The language PROLOG is used to implement canonical graphs and the processing tools of the extendible graph processor. Applications of the conceptual graph model are highlighted with a detailed example of schema/script processing.

Recursive modal unification for reasoning with knowledge using a graph representation

Abstract In this paper a modal reasoning mechanism adapted to the formalism of conceptual graphs is reported. The significance of the work lies in (1) unification of epistemological structures represented as nested graphs; (2) recursive nature of algorithm; and (3) full compatibility of the new sub-system with existing tools for knowledge engineering using conceptual graphs. The mechanism for modal reasoning comprises three elements: (a) generation of conceptual graph equivalents of modal literals; (b) unification of the modal literals; and (c) generation of the conceptual graph equivalent of the resolvent literals.

Collaborative knowledge management requirements for experiential learning (CKM)

Exploratory studies in collaborative knowledge management (CKM) across four domains have identified significantly expanded research requirements for experiential learning. This paper reports preliminary conclusions/propositions. The quality of collaborative (group) learning, particularly in experiential processes such as problem solving and professional practice, requires the innovative support of knowledge-mediated human interaction requirements and the associated sharing of knowledge between participants.

Knowledge mediation in software quality engineering

The risk of failure of the software development process remains high despite many attempts to improve the quality of software engineering. Contemporary approaches to process assurance, such as the capability maturity model have not prevented systemic failures, nor have project management methodologies provided guarantees of software quality. The paper proposes an approach to software quality assurance based on a knowledge mediated concurrent audit, which incorporates essential feedback processes. Through a tightly integrated approach to quality audit, programmers would be empowered to use any chosen methodology to advantage, supported by intelligent monitoring of the essential interactions which occur in the development process. An experimental application implementing some aspects of the proposal is described.

Modelling expert resource management systems

The data structuring mechanisms provided by the current problem solving environment have been found to satisfy modeling the problem solutions for common decision making processes of a static nature, whereas problems of a complex nature (e.g. resource management and optimization) need an abstract data structuring mechanism capable of emulating real‐life objects (e.g. human managers). This paper explains the concept of developing a new data structure based on a semantic network called SYSTEM MAP and shows how this novel data structure can be used to model expert resource management systems of a particularly hierarchical type.

A Self-Organising Dictionary For Conceptual Structures

The provision of greater intelligence in the storage and reorganisation of knowledge bases is seen to be fundamental to future directions in automated knowledge acquisition, discourse understanding and the design of conceptual processors. In this paper, we report the design and implementation of a self-organising conceptual dictionary for conceptual structures as the basis of an intelligent knowledge-base manager. The dictionary is a connected, directed graph with capabilities for storing graphs, indexing incoming structures, generalising between incoming structures and existing (indexed) structures, identification of graph-subgraph relationships, pattern matching and proposing new structures. Existing work on self-organising retrieval systems is compared and contrasted with our new design.