Search (14 results, page 1 of 1)

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Classification

006.7 22

Date

7. 3.2007 19:30:22

DDC

006.7 22

Footnote

The final part of the book discusses research in multimedia content management systems and the semantic web, and presents examples and applications for semantic multimedia analysis in search and retrieval systems. These chapters describe example systems in which current projects have been implemented, and include extensive results and real demonstrations. For example, real case scenarios such as ECommerce medical applications and Web services have been introduced. Topics in natural language, speech and image processing techniques and their application for multimedia indexing, and content-based retrieval have been elaborated upon with extensive examples and deployment methods. The editors of the book themselves provide the readers with a chapter about their latest research results on knowledge-based multimedia content indexing and retrieval. Some interesting applications for multimedia content and the semantic web are introduced. Applications that have taken advantage of the metadata provided by MPEG7 in order to realize advance-access services for multimedia content have been provided. The applications discussed in the third part of the book provide useful guidance to researchers and practitioners properly planning to implement semantic multimedia analysis techniques in new research and development projects in both academia and industry. A fourth part should be added to this book: performance measurements for integrated approaches of multimedia analysis and the semantic web. Performance of the semantic approach is a very sophisticated issue and requires extensive elaboration and effort. Measuring the semantic search is an ongoing research area; several chapters concerning performance measurement and analysis would be required to adequately cover this area and introduce it to readers."

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LCSH

Electronic reference services (Libraries)

Subject

Electronic reference services (Libraries)

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Abstract

Das Buch präsentiert die vielfältigen Möglichkeiten von Open-Source-Software zur Unterstützung von Wissensmanagement. Der Autor erläutert die Grundlagen und Einsatzmöglichkeiten von Open-Source-Software beim Knowledge Management und entwickelt auf Grund von Analysen konkreter Open-Source-Produkte Entscheidungskriterien und Anleitungen für die Verbesserung von Knowledge Management- und Open-Source-Software. Kosteneinsparungen und Effizienz finden dabei besondere Beachtung.

Content

Inhalt: Definitionen von Knowledge, Knowledge Management und Open Source.- Vision eines Knowledge Management- (KM-) Systems.- Vorhandene Open-Source-Basis.- Technische Basis.- Start mit einem Groupware System.- Alternativ Start mit einem Content-Management-System.- Einbinden von Groupware oder CMS bzw. Erweitern um DMS.- Weiterer Ausbau.- Zusammenfassungen, Abschluss und Ausblick.- Literaturverzeichnis.- Index.

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Abstract

Ontologies have been developed and investigated for quite a while now in artificial intelligente and natural language processing to facilitate knowledge sharing and reuse. More recently, the notion of ontologies has attracied attention from fields such as intelligent information integration, cooperative information systems, information retrieval, electronic commerce, and knowledge management. The author systematicaliy introduces the notion of ontologies to the non-expert reader and demonstrates in detail how to apply this conceptual framework for improved intranet retrieval of corporate information and knowledge and for enhanced Internet-based electronic commerce. In the second part of the book, the author presents a more technical view an emerging Web standards, like XML, RDF, XSL-T, or XQL, allowing for structural and semantic modeling and description of data and information.

LCSH

Database management

Subject

Database management

Theme

Information Resources Management

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Date

23. 3.2008 19:10:22

Footnote

In 13 chapters, Part Two provides an introduction to KDD, an overview of data mining techniques, and examples of the usefulness of data model visualizations. The importance of visualization throughout the KDD process is stressed in many of the chapters. In particular, the need for measures of visualization effectiveness, benchmarking for identifying best practices, and the use of standardized sample data sets is convincingly presented. Many of the important data mining approaches are discussed in this complementary context. Cluster and outlier detection, classification techniques, and rule discovery algorithms are presented as the basic techniques common to the KDD process. The potential effectiveness of using visualization in the data modeling process are illustrated in chapters focused an using visualization for helping users understand the KDD process, ask questions and form hypotheses about their data, and evaluate the accuracy and veracity of their results. The 11 chapters of Part Three provide an overview of the KDD process and successful approaches to integrating KDD, data mining, and visualization in complementary domains. Rhodes (Chapter 21) begins this section with an excellent overview of the relation between the KDD process and data mining techniques. He states that the "primary goals of data mining are to describe the existing data and to predict the behavior or characteristics of future data of the same type" (p. 281). These goals are met by data mining tasks such as classification, regression, clustering, summarization, dependency modeling, and change or deviation detection. Subsequent chapters demonstrate how visualization can aid users in the interactive process of knowledge discovery by graphically representing the results from these iterative tasks. Finally, examples of the usefulness of integrating visualization and data mining tools in the domain of business, imagery and text mining, and massive data sets are provided. This text concludes with a thorough and useful 17-page index and lengthy yet integrating 17-page summary of the academic and industrial backgrounds of the contributing authors. A 16-page set of color inserts provide a better representation of the visualizations discussed, and a URL provided suggests that readers may view all the book's figures in color on-line, although as of this submission date it only provides access to a summary of the book and its contents. The overall contribution of this work is its focus an bridging two distinct areas of research, making it a valuable addition to the Morgan Kaufmann Series in Database Management Systems. The editors of this text have met their main goal of providing the first textbook integrating knowledge discovery, data mining, and visualization. Although it contributes greatly to our under- standing of the development and current state of the field, a major weakness of this text is that there is no concluding chapter to discuss the contributions of the sum of these contributed papers or give direction to possible future areas of research. "Integration of expertise between two different disciplines is a difficult process of communication and reeducation. Integrating data mining and visualization is particularly complex because each of these fields in itself must draw an a wide range of research experience" (p. 300). Although this work contributes to the crossdisciplinary communication needed to advance visualization in KDD, a more formal call for an interdisciplinary research agenda in a concluding chapter would have provided a more satisfying conclusion to a very good introductory text.

Series

Morgan Kaufmann series in data management systems

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Abstract

This book provides relevant content in the areas of information retrieval systems, services, and research; covering topics such as social tagging, collaborative querying, social network analysis, subjective relevance judgments, and collaborative filtering. Answering the increasing demand for authoritative resources on Internet technologies, this will make an indispensable addition to any library collection

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Abstract

Extracting content from text continues to be an important research problem for information processing and management. Approaches to capture the semantics of text-based document collections may be based on Bayesian models, probability theory, vector space models, statistical models, or even graph theory. As the volume of digitized textual media continues to grow, so does the need for designing robust, scalable indexing and search strategies (software) to meet a variety of user needs. Knowledge extraction or creation from text requires systematic yet reliable processing that can be codified and adapted for changing needs and environments. This book will draw upon experts in both academia and industry to recommend practical approaches to the purification, indexing, and mining of textual information. It will address document identification, clustering and categorizing documents, cleaning text, and visualizing semantic models of text.

Classification

ST 270 Informatik / Monographien / Software und -entwicklung / Datenbanken, Datenbanksysteme, Data base management, Informationssysteme

RVK

ST 270 Informatik / Monographien / Software und -entwicklung / Datenbanken, Datenbanksysteme, Data base management, Informationssysteme

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Abstract

As the World Wide Web continues to expand, it becomes increasingly difficult for users to obtain information efficiently. Because most search engines read format languages such as HTML or SGML, search results reflect formatting tags more than actual page content, which is expressed in natural language. Spinning the Semantic Web describes an exciting new type of hierarchy and standardization that will replace the current "Web of links" with a "Web of meaning." Using a flexible set of languages and tools, the Semantic Web will make all available information - display elements, metadata, services, images, and especially content - accessible. The result will be an immense repository of information accessible for a wide range of new applications. This first handbook for the Semantic Web covers, among other topics, software agents that can negotiate and collect information, markup languages that can tag many more types of information in a document, and knowledge systems that enable machines to read Web pages and determine their reliability. The truly interdisciplinary Semantic Web combines aspects of artificial intelligence, markup languages, natural language processing, information retrieval, knowledge representation, intelligent agents, and databases.

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Date

21. 3.2008 19:10:22

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LCSH

Knowledge management

Subject

Knowledge management

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Abstract

This book constitutes the refereed proceedings of the joint 6th International Semantic Web Conference, ISWC 2007, and the 2nd Asian Semantic Web Conference, ASWC 2007, held in Busan, Korea, in November 2007. The 50 revised full academic papers and 12 revised application papers presented together with 5 Semantic Web Challenge papers and 12 selected doctoral consortium articles were carefully reviewed and selected from a total of 257 submitted papers to the academic track and 29 to the applications track. The papers address all current issues in the field of the semantic Web, ranging from theoretical and foundational aspects to various applied topics such as management of semantic Web data, ontologies, semantic Web architecture, social semantic Web, as well as applications of the semantic Web. Short descriptions of the top five winning applications submitted to the Semantic Web Challenge competition conclude the volume.

LCSH

Knowledge management / Congresses

Subject

Knowledge management / Congresses

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Abstract

Thematisch haben die beiden Herausgeber ihre Beitragssammlung in vier Bereiche aufgeteilt: zuerst der Einstieg ins Thema Web 2.0 mit sechs Beiträgen - die Konvergenz von Semantic Web und Web 2.0, Auswirkungen auf die Gesellschaft, Technologien und jeweils die entsprechenden Perspektiven; gute, faktenbasierte Überblicke, die das Buch auf eine solide Grundlage stellen und reichhaltiges Wissen für ansonsten meist oberflächlich geführte Diskussionen zum Thema liefern. Der zweite Themenbereich ist dann den Technologien und Methodem gewidmet: Wie sieht die Technik hinter der "Semantik" im Web aus? Wissensmodellierung, Beispiele wie die Expertensuche, Content Management Systeme oder semantische Wikis. In Teil drei werfen die Autoren dann einen Blick auf bestehende Anwendungen im aktuellen Social Semantic Web und geben einen Ausblick auf deren zukünftige Entwicklungen. Im abschließenden Teil vier schließlich geben die Autoren der drei Beiträge einen Überblick zum "Social Semantic Web" aus sozialwissenschaftlicher Perspektive. Zusammenfassungen am Anfang jedes Beitrags, gute Strukturen zusätzliche Quellen und auch die gut lesbare Länge der Beiträge - angenehme verständlich und konsumierbar. Blumauer und Pellegrini haben auch mit Social Semantic Web: Web 2.0 - Was nun? ganze Arbeit geleistet und ein breites Spektrum an Perspektiven, Meinungen und Quellen zusammengetragen - das Ergebnis ist stellenweise durchaus heterogen und nicht jeder Beitrag muss gefallen, doch das Gesamtergebnis stellt die von den Herausgebern thematisierte heterogene Welt des Social Semantic Web realistisch dar. Wer in Zukunft nicht nur "gefährliches Halbwissen" um das Web 2.0 und das Semantic Web von sich geben will, sollte Social Semantic Web: Web 2.0 - Was nun? gelesen haben!

Content

Semantische Content Management Systeme - Erich Gams und Daniel Mitterdorfer Tag-Recommender gestützte Annotation von Web-Dokumenten - Andreas Blumauer und Martin Hochmeister Semantische Wikis - Sebastian Schaffert, François Bry, Joachim Baumeister und Malte Kiesel Semantische Mashups auf Basis Vernetzter Daten - Sören Auer, Jens Lehmann und Christian Bizer Web-gestütztes Social Networking am Beispiel der "Plattform Wissensmanagement" - Stefanie N. Lindstaedt und Claudia Thurner Explorative Netzwerkanalyse im Living Web - Gernot Tscherteu und Christian Langreiter Semantic Desktop - Leo Sauermann, Malte Kiesel, Kinga Schumacher und Ansgar Bernardi Social Bookmarking am Beispiel BibSonomy - Andreas Hotho, Robert Jäschke, Dominik Benz, Miranda Grahl, Beate Krause, Christoph Schmitz und Gerd Stumme Semantic Wikipedia - Markus Krötzsch und Denny Vrandecic Die Zeitung der Zukunft - Christoph Wieser und Sebastian Schaffert Das Semantic Web als Werkzeug in der biomedizinischen Forschung - Holger Stenzhorn und Matthias Samwald

Footnote

Vgl.: http://www.springer.com/computer/database+management+%26+information+retrieval/book/978-3-540-72215-1.

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Abstract

It may seem a strange match - AI and crop irrigation or AI and the Serengeti lions but researchers in artificial intelligence envision expert systems as a new technology for capturing the knowledge and reasoning process of experts in agriculture, wildlife management and many other fields. These computer programmes have a relevance for developing nations that desire to close the gap between themselves and the richer nations of the world. Despite the value and appeal of expert systems for economic and technological development, Schoenhoff dicloses how this technology reflects the Western preoccupation with literacy and rationality. When expert systems are introduced into developing nations, they must interact with persons who reason and articulate their knowledge in ways unfamiliar to high-tech cultures. Knowledge, particularly in poor and traditional communities, may be expressed in proverbs rather than propositions or in folklore rather that formulas. Drawing upon diverse disciplines, the author explores whether such indigenous knowledge can be incorporated into the formal language and artificial rationality of the computer - and the imperative for working toward this incorporation.

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Series

Advances in information systems and management science; 39