Basics of qualitative research: Grounded theory procedures and techniques

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.

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Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

Data Mining Practical Machine Learning Tools and Techniques

How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

A Tool to Support Living Ontologies

This paper investigates the role of ontologies as a central part of an architecture to repurpose existing material from the web. A prototype system called ArtEquAKT is presented, which combines information extraction, knowledge management and consolidation techniques and adaptive document generation. All of these components are co-ordinated using one central ontology, providing a common vocabulary for describing the information fragments as they are processed. Each of the components of the architecture is described in detail and an evaluation of the system discussed. Conclusions are drawn as to the effectiveness of such an approach and further challenges are outlined.

/pdf/ontologies-as-facilitators-for-repurposing-web-documents-45opr4bza7.pdf

Ontologies as facilitators for repurposing web documents

In this paper we propose a distributed knowledge management framework for semantics and knowledge creation, population and reuse on the Grid. Its objective is to evolve the Grid towards the Semantic Grid with the ultimate purpose of facilitating problem solving in e-Science. The framework uses ontology as the conceptual backbone and adopts the service-oriented computing paradigm for information-level and knowledge-level computation. We further present a semantics-based approach to problem solving, which exploits the rich semantic information of grid resource descriptions for resource discovery, instantiation and composition. The framework and approach has been applied to a UK e-Science project - Grid Enabled Engineering Design Search and Optimisation in Engineering (GEODISE). An ontology-enabled Problem Solving Environment (PSE) has been developed in GEODISE to leverage the semantic content of GEODISE resources and the Semantic Grid infrastructure for engineering design. Implementation and initial experimental results are reported.

/pdf/semantics-assisted-problem-solving-on-the-semantic-grid-15vn8anvmi.pdf

Semantics-assisted problem solving on the semantic grid

In this paper we confront the divide between the ontologies developed from the requirement of comprehensive and general domain coverage and those devised to meet application-specific requirements. While the generalists typically attach philosophical sophistication to their approach, in supposed contrast to the narrow remit chosen by the application-bound knowledge engineers, we would like to indicate that the latter practice can often reflect a multi-faceted rationale, nuanced by the requirements of the domain. We demonstrate how the necessity of placing ontology-based systems with the work-practices of domain experts introduces unique demands on design rationales and enforces, often implicitly, a philosophical assessment of the necessary concepts and relations that balance the generality and specificity. Such demands are not addressed by generic approaches to modelling the reality of a domain. Indeed, we articulate the philosophical and practical considerations that we have taken into account when developing an application-specific ontology. We would certainly hope that our experiences can be of help to the development of ontologies in similar applications.

Reflections on a medical ontology

We present two related computational models of ocular dominance column formation. Both address nervous system plasticity in terms of sprouting and retraction of axonal processes rather than changes in synaptic strength implied by synapse-specific Hebbian models. We employ statistical mechanics to simulate changes in the pattern of network connectivity. Our formalism uses the concept of an energy function, which we interpret as related to the levels of targetgenerated neurotrophins for which afferents compete. In contrast synapse-specific Hebbian models impose synaptic normalization, for which there is little experimental evidence, in order to induce competition. Our models make many predictions which require experimental investigation. We suggest that the absence of monocular deprivation effects in the optic tectum may be due to a tendency of amphibian retinal ganglion cells to preserve the complexity of their terminal arbors. One model raises the possibility that boundaries separating columns in the mammalian cortex are poorly innervated if they have been formed by complete but asynchronous retinal activation. Both models exhibit a phase transition, suggesting a discontinuity in the transition from a binocular cortex to one possessing ocular dominance columns. Finally, our other model could account for the perpendicularity of ocular dominance columns to the boundary of the primary visual cortex while admitting of less ordered central patterns.

Nigel Shadbolt

Papers

A Tool to Support Living Ontologies

Ontologies as facilitators for repurposing web documents

Semantics-assisted problem solving on the semantic grid

Reflections on a medical ontology

Axonal Processes and Neural Plasticity. I: Ocular Dominance Columns