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.

/pdf/toward-the-next-generation-of-recommender-systems-a-survey-4wwsv77rns.pdf

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

Certification is the process of showing that software meets its requirements and is fit for purpose. It occurs in sensitive contexts where it is essential for safety or other reasons that the software should never fail. Certifying KBSs can be seen as especially problematic, because KBSs' output, being heuristic rather than algorithmic, is less predictable than that of conventional software. By developing a detailed and extended case study, the authors show that KBS development governed by CommonKADS is, with very little adjustment, satisfactory for KBS certification.

Certifying KBSs: Using CommonKADS to Provide Supporting Evidence for Fitness for Purpose of KBSs

This paper firstly utilizes the ontology such as WordNet to build the semantic structures of text documents, and then enhance the semantic similarity among them Because the correlations between documents make them lie on or close to a smooth low-dimensional manifold so that documents can be well characterized by a manifold within the space of documents, we calculate the similarity between any two semantically structured documents with respect to the intrinsic global manifold structure This idea has been validated in the conducted text categorization experiments on patent documents.

Ontology-Based similarity between text documents on manifold

Experiments in Knowledge Elicitation

The World Wide Web is a potentially valuable source of information about the cognitive characteristics of cultural groups. However, attempts to use the Web in the context of cultural modeling activities are hampered by the large-scale nature of the Web and the current dominance of natural language formats. In this paper, we outline an approach to support the exploitation of the Web for cultural modeling activities. The approach begins with the development of qualitative cultural models (which describe the beliefs, concepts and values of cultural groups), and these models are subsequently used to develop an ontology-based information extraction capability. Our approach represents an attempt to combine conventional approaches to information extraction with epidemiological perspectives of culture and network-based approaches to cultural analysis. The approach can be used, we suggest, to support the development of models providing a better understanding of the cognitive characteristics of particular cultural groups.

/pdf/using-web-based-knowledge-extraction-techniques-to-support-1981rth8ac.pdf

Using web-based knowledge extraction techniques to support cultural modeling

This poster paper presents the design and implementation of an RDFS reasoner based on a backward chaining approach and implemented on a clustered RDF triplestore. The system presented, called 4sr, uses 4store as base infrastructure. In order to achieve a highly scalable system we implemented the reasoning at the lowest level of the quad store, the bind operation. The bind operation in 4sr traverses the quad store indexes matching or expanding the query variables with awareness of the RDFS semantics.

/pdf/4sr-scalable-decentralized-rdfs-backward-chained-reasoning-hq7ql4b17f.pdf

Nigel Shadbolt

Papers

Certifying KBSs: Using CommonKADS to Provide Supporting Evidence for Fitness for Purpose of KBSs

Ontology-Based similarity between text documents on manifold

Experiments in Knowledge Elicitation

Using web-based knowledge extraction techniques to support cultural modeling

4sr - scalable decentralized RDFS backward chained reasoning