Similarity and explanation for dynamic telecommunication engineer support.

Understanding similarity between different examples is a crucial aspect of Case-Based Reasoning (CBR) systems, but learning representations optimised for similarity comparisons can be difficult. CBR systems typically rely on separate algorithms to learn representations for cases and to compare those representations, as symbolised by the vocabulary and similarity knowledge containers respectively. Deep Metric Learners (DMLs) are a branch of deep learning architectures which learn a representation optimised for similarity comparison by leveraging direct case comparisons during training. In this thesis we explore the symbiotic relationship between these two fields of research. Firstly we examine what can be learned from traditional CBR research to improve the training of DMLs through training strategies. We then examine how DMLs can fill the traditionally separate roles of the vocabulary and similarity knowledge containers. We perform this exploration on the real-world problem of experience transfer between experts and non-experts on service provisioning for telecommunication organisations. This problem is also revealing about the requirements for practical applications to be explainable to their intended user group. With that in mind, we conclude this thesis with work towards the development of an explanation framework designed to explain the recommendations of similarity-based classifiers. We support this practical contribution with an exploration of similarity knowledge to support autonomous measurement of explanation quality

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

Science as systems learning. Some reflections on the cognitive and communicational aspects of science

This paper undertakes a theoretical investigation of the 'learning' aspect of science as opposed to the 'knowledge' aspect. The practical background of the paper is in agricultural systems research – an area of science that can be characterised as 'systemic' because it is involved in the development of its own subject area, agriculture. And the practical purpose of the theoretical investigation is to contribute to a more adequate understanding of science in such areas, which can form a basis for developing and evaluating systemic research methods, and for determining appropriate criteria of scientific quality. Two main perspectives on science as a learning process are explored: research as the learning process of a cognitive system, and science as a social, communicational system. A simple model of a cognitive system is suggested, which integrates both semiotic and cybernetic aspects, as well as a model of selfreflective learning in research, which entails moving from an inside 'actor' stance to an outside 'observer' stance, and back. This leads to a view of scientific knowledge as inherently contextual and to the suggestion of reflexive objectivity and relevance as two related key criteria of good science

Towards a systemic research methodology in agriculture: Rethinking the role of values in science

The recent drastic development of agriculture, together with the growing societal interest in agricultural practices and their consequences, pose a challenge to agricultural science. There is a need for rethinking the general methodology of agricultural research. This paper takes some steps towards developing a systemic research methodology that can meet this challenge – a general self-reflexive methodology that forms a basis for doing holistic or (with a better term) wholeness-oriented research and provides appropriate criteria of scientific quality. From a philosophy of research perspective, science is seen as an interactive learning process with both a cognitive and a social communicative aspect. This means, first of all, that science plays a role in the world that it studies. A science that influences its own subject area, such as agricultural science, is named a systemic science. From this perspective, there is a need to reconsider the role of values in science. Science is not objective in the sense of being value-free. Values play, and ought to play, an important role in science – not only in form of constitutive values such as the norms of good science, but also in the form of contextual values that enter into the very process of science. This goes against the traditional criterion of objectivity. Therefore, reflexive objectivity is suggested as a new criterion for doing good science, along with the criterion of relevance. Reflexive objectivity implies that the communication of science must include the cognitive context, which comprises the societal, intentional, and observational context. In accordance with this, the learning process of systemic research is shown as a self-reflexive cycle that incorporates both an involved actor stance and a detached observer stance. The observer stance forms the basis for scientific communication. To this point, a unitary view of science as a learning process is employed. A second important perspective for a systemic research methodology is the relation between the actual, different, and often quite separate kinds of science. Cross-disciplinary research is hampered by the idea that reductive science is more objective, and hence more scientific, than the less reductive sciences of complex subject areas – and by the opposite idea that reductive science is necessarily reductionistic. Taking reflexive objectivity as a demarcator of good science, an inclusive framework of science can be established. The framework does not take the established division between natural, social and human science as a primary distinction of science. The major distinction is made between the empirical and normative aspects of science, corresponding to two key cognitive interests. Two general methodological dimensions, the degree of reduction of the research world and the degree of involvement in the research world, are shown to span this framework. The framework can form a basis for transdisciplinary work by way of showing the relation between more and less reductive kinds of science and between more detached and more involved kinds of science and exposing the abilities and limitations attendant on these methodological differences

Integrating descriptions of knowledge management learning activities into large ontological structures: A case study

Ontologies have been recognized as a fundamental infrastructure for advanced approaches to Knowledge Management (KM) automation, and the conceptual foundations for them have been discussed in some previous reports. Nonetheless, such conceptual structures should be properly integrated into existing ontological bases, for the practical purpose of providing the required support for the development of intelligent applications. Such applications should ideally integrate KM concepts into a framework of commonsense knowledge with clear computational semantics. In this paper, such an integration work is illustrated through a concrete case study, using the large OpenCyc knowledge base. Concretely, the main elements of the Holsapple & Joshi KM ontology and some existing work on e-learning ontologies are explicitly linked to OpenCyc definitions, providing a framework for the development of functionalities that use the built-in reasoning services of OpenCyc in KM ctivities. The integration can be used as the point of departure for the engineering of KM-oriented systems that account for a shared understanding of the discipline and rely on public semantics provided by one of the largest open knowledge bases available