The Web Of Knowledge

This essay relates together, in a clear and concise manner, four major groups of grammatical meanings — evidentiality for information source, egophopricity for access to knowledge, mirativity for expectation of knowledge, and epistemic modality for attitude to knowledge.

Language is a human universal reflecting our deeply social nature. Among its essential functions, language enables us to quickly and efficiently share information. We tell each other that many things are true—that is, we routinely make assertions. Information shared this way plays a critical role in the decisions and plans we make. In Knowledge and the Norm of Assertion, a distinguished philosopher and cognitive scientist investigates the rules or norms that structure our social practice of assertion. Combining evidence from philosophy, psychology, and biology, John Turri shows that knowledge is the central norm of assertion and explains why knowledge plays this role. Concise, comprehensive, non-technical, and thoroughly accessible, this volume quickly brings readers to the cutting edge of a major research program at the intersection of philosophy and science. It presupposes no philosophical or scientific training. It will be of interest to philosophers and scientists, is suitable for use in graduate and undergraduate courses, and will appeal to general readers interested in human nature, social cognition, and communication.

This new ASIST monograph is the first to comprehensively address the history, theory, and practical applications of citation analysis, a field which has grown from Garfield's seed of an idea, and to examine its impact on scholarly research forty years after its inception. In bringing together the analyses, insights, and reflections of more than thirty-five leading lights, editors Cronin and Atkins have produced both a comprehensive survey of citation indexing and its applications and a beautifully-realized tribute to Eugene Garfield and his vision, in honor of his seventy-fifth birthday.

Knowledge-Based Information Retrieval and Filtering from the Web contains fifteen chapters, contributed by leading international researchers, addressing the matter of information retrieval, filtering and management of the information on the Internet. The research presented deals with the need to find proper solutions for the description of the information found on the Internet, the description of the information consumers need, the algorithms for retrieving documents (and indirectly, the information embedded in them), and the presentation of the information found. The chapters include: -Ontological representation of knowledge on the WWW; -Information extraction; -Information retrieval and administration of distributed documents; -Hard and soft modeling based knowledge capture; -Summarization of texts found on the WWW; -User profiles and personalization for web-based information retrieval system; -Information retrieval under constricted bandwidth; -Multilingual WWW; -Generic hierarchical classification using the single-link clustering; -Clustering of documents on the basis of text fuzzy similarity; -Intelligent agents for document categorization and adaptive filtering; -Multimedia retrieval and data mining for E-commerce and E-business; -A Web-based approach to competitive intelligence; -Learning ontologies for domain-specific information retrieval; -An open, decentralized architecture for searching for, and publishing information in distributed systems.

Featuring contributions from staff and associates of the Knowledge Media Institute at the UK Open University, this text provides a glimpse into the wide variety of projects undertaken in the development and assessment of distance learning technologies.

First Published in 2000. Routledge is an imprint of Taylor & Francis, an informa company.

Websites are a central part of today’s business world; however, with the vast amount of information that constantly changes and the frequency of required updates, this can come at a high cost to modern businesses. Web Data Mining and the Development of Knowledge-Based Decision Support Systems is a key reference source on decision support systems in view of end user accessibility and identifies methods for extraction and analysis of useful information from web documents. Featuring extensive coverage across a range of relevant perspectives and topics, such as semantic web, machine learning, and expert systems, this book is ideally designed for web developers, internet users, online application developers, researchers, and faculty.

The definitive book on mining the Web from the preeminent authority.