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

Wiki systems have developed over the past years as lightweight, community-editable, web-based hypertext systems. With the emergence of semantic wikis such as Semantic MediaWiki [6], these collections of interlinked documents have also gained a dual role as ad-hoc RDF [7] graphs. However, their roots lie in the limited hypertext capabilities of the World Wide Web [1]: embedded links, without support for features like composite objects or transclusion. Collaborative editing on wikis has been hampered by redundancy; much of the effort spent on Wikipedia is used keeping content synchronised and organised.[3] We have developed a model for a system, which we have prototyped and are evaluating, which reintroduces ideas from the field of hypertext to help alleviate this burden.

In this paper, we present a model for what we term an 'open semantic hyperwiki' system, drawing from both past hypermedia models, and the informal model of modern semantic wiki systems. An 'open semantic hyperwiki' is a reformulation of the popular semantic wiki technology in terms of the long-standing field of hypermedia, which then highlights and resolves the omissions of hypermedia technology made by the World Wide Web and the applications built around its ideas. In particular, our model supports first-class linking, where links are managed separately from nodes. This is then enhanced by the system's ability to embed links into other nodes and separate them out again, allowing for a user editing experience similiar to HTML-style embedded links, while still gaining the advantages of separate links. We add to this transclusion, which allows for content sharing by including the content of one node into another, and edit-time transclusion, which allows users to edit pages containing shared content without the need to follow a sequence of indirections to find the actual text they wish to modify. Our model supports more advanced linking mechanisms, such as generic links, which allow words in the wiki to be used as link endpoints.

The development of this model has been driven by our prior experimental work on the limitations of existing wikis and user interaction.We have produced a prototype implementation which provides first-class links, transclusion, and generic links.

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A model for open semantic hyperwikis

This paper outlines three techniques and tools which have recently arisen from Semantic Web research in the International Technology Alliance. The first of these techniques is POAF (Portable Ontology Aligned Fragments), which addresses issues relating to the portability and usage of ontology alignments. POAF uses an ontology fragmentation strategy to achieve portability, and it enables subsequent usage of ontologies through a form of automated ontology modularization. The second technique, SWEDER (Semantic Wrapping of Existing Data sources with Embedded Rules), is grounded in the creation of lightweight ontologies to semantically wrap existing data sources, which facilitates rapid semantic integration through representational homogeneity and embedded rules. The third technique is NITELIGHT, which is a tool that has been created to better support end users with respect to the creation and editing of semantic queries. NITELIGHT uses a visual query language, called vSPARQL, and it is based on the W3C SPARQL query language specification. NITELIGHT supports end users by providing a set of graphical notations that represent semantic query language constructs. These enable the user to combine ontology navigation capabilities with graphical query visualisation techniques.

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Using Semantic Web Technologies to Support Information Processing and Coalition Decision-Making

/pdf/faculty-of-engineering-science-and-mathematics-school-of-1oqpq4hnsr.pdf

Faculty of Engineering, Science and Mathematics School of Electronics and Computer Science Intelligence, Agents, Multimedia Group A mini-thesis submitted for transfer from MPhil to PhD

The World Wide Web serves as a valuable source of culture-relevant information, which can be used to support cultural modeling and analysis activities. Part of the challenge in exploiting the Web as a source of culture-relevant information relates to the need to detect and extract information about beliefs, attitudes, and values from a variety of different resources. The Web, thus, features a rich variety of information resources, and these are seldom categorized with respect to the dimensions in which cultural analysts are interested. Exploiting the Web as a source of culture-relevant information therefore requires techniques and approaches that enable cultural analysts to extract relevant information and organize extracted content in various ways. In this paper, we outline an approach to assist cultural analysts in the extraction and organization of relevant information. We show techniques that can be used to extract information of the attitudes, beliefs, and values of individuals, and how this data can, in turn, be used to support cultural modeling and analysis.

/pdf/detection-of-cognitive-features-from-web-resources-in-1771hafiug.pdf

Detection of cognitive features from web resources in support of cultural modeling and analysis

While we witness an explosion of exploration tools for simple datasets on Web 2.0 designed for use by ordinary citizens, the goal of a usable interface for supporting navigation and sense-making over arbitrary linked data has remained elusive. The purpose of this paper is to analyse why - what makes exploring linked data so hard? Through a user-centered use case scenario, we work through requirements for sense making with data to extract functional requirements and to compare these against our tools to see what challenges emerge to deliver a useful, usable knowledge building experience with linked data. We present presentation layer and heterogeneous data integration challenges and offer practical considerations for moving forward to effective linked data sensemaking tools.

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Nigel Shadbolt

Papers

A model for open semantic hyperwikis

Using Semantic Web Technologies to Support Information Processing and Coalition Decision-Making

Faculty of Engineering, Science and Mathematics School of Electronics and Computer Science Intelligence, Agents, Multimedia Group A mini-thesis submitted for transfer from MPhil to PhD

Detection of cognitive features from web resources in support of cultural modeling and analysis

Will this work for Susan? Challenges for Delivering Usable and Useful Generic Linked Data Browsers