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

In the current research environment, funding agencies are increasingly required to demonstrate that the projects they fund represent value for money. When funds are disbursed in a speculative manner, in order to stimulate interdisciplinary collaboration, the determination of value for money relies on evidence that shows the generation of new collaborations. This paper summarises the work we carried out on behalf of the Engineering and Physical Sciences Research Council (EPSRC), in which we have implemented a set of applications to enable the research council to examine the existence and nature of collaborations between researchers. We have used Semantic Web technologies to construct a flexible application framework to provide multiple complementary visualisations of the data, while separating the issues of knowledge acquisition and curation from the more user-centric interface requirements.

Monitoring research collaborations using semantic web technologies Conference or Workshop Item

Personal information and the act of publishing multimedia artifacts to the World Wide Web is becoming more and more observable. This report presents an infrastructure for the capturing and exploitation of personal metadata to drive research into context aware systems. I aim to expose ongoing research in the areas of capture of personal experiences, context aware systems, multimedia annotation systems, narrative generation, and that of Semantic Web enabling technologies. This report details the work underway towards the goal of creating a multidomain contextual log, and is followed by a discussion of how this work is being used to drive the development of a multimedia asset management system infrastructure. Practical work already completed as part of this project is then described, focusing on the areas of data integration, infrastructure, and personal photo collection annotation, all in the Semantic Web context. The future plan draws together the work completed and presents how the autobiographical context captured will be evaluated to provide insight into the utility of the metadata harnessed to aid human memory management. This is followed by the proposed evaluation of how suitable semi-automatically captured metadata can be to facilitate the construction of a narrative of a person’s observable life.

An Investigation into Automatically Captured Autobiographical Metadata, and the Support for Autobiographical Narrative Generation. Mini-Thesis: PhD upgrade report

Ontology is important in sharing and reusing knowledge It also plays a crucial role in the development of Semantic Web The paper discusses the DL(Description Logic) and graph view on ontology Different perspectives have different models and approaches on ontology mapping The paper presents how the two different approaches handle ontology mapping, respectively We argue that a combination of the two views can lead to a better solution in ontology mapping

Towards Ontology Mapping: DL View or Graph View?

During the past several years, semantic web technologies have been applied to facilitate the sharing of data in new and unexpected ways. Various heterogeneous data are assigned well-defined meaning by ontologies and expressed with RDF triples so that they are are able to be integrated and extracted across multiple sources. As the order of magnitude of triples hosted is higher and higher, and considering other essential issues (e.g. copyright, security) in application domains, a distributed RDF data management approach is more appropriate for knowledge sharing and integration. In this paper, we present a knowledge bus infrastructure - a general solution of locating and extracting knowledge elements from distributed sources on-demand rather than loading all of RDF triples into a large central triple store in advance. A semantic matching approach is discussed to support the key function of automatic knowledge source location. The knowledge bus infrastructure as well as the semantic matching approach has been adopted in the CFMS project, enabling a rapid information search and access for the engineering domain.

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A Semantic Matching Approach for Distributed RDF Data Query on a Knowledge Bus

How much are we to trust a decision made by an AI algorithm? Trusting an algorithm without cause may lead to abuse, and mistrusting it may similarly lead to disuse. Trust in an AI is only desirable if it is warranted; thus, calibrating trust is critical to ensuring appropriate use. In the name of calibrating trust appropriately, AI developers should provide contracts specifying use cases in which an algorithm can and cannot be trusted. Automated explanation of AI outputs is often touted as a method by which trust can be built in the algorithm. However, automated explanations arise from algorithms themselves, so trust in these explanations is similarly only desirable if it is warranted. Developers of algorithms explaining AI outputs (xAI algorithms) should provide similar contracts, which should specify use cases in which an explanation can and cannot be trusted.

Nigel Shadbolt

Papers

Monitoring research collaborations using semantic web technologies Conference or Workshop Item

An Investigation into Automatically Captured Autobiographical Metadata, and the Support for Autobiographical Narrative Generation. Mini-Thesis: PhD upgrade report

Towards Ontology Mapping: DL View or Graph View?

A Semantic Matching Approach for Distributed RDF Data Query on a Knowledge Bus

Trust Explanations to Do What They Say