Search (89 results, page 1 of 5)

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Source

GMD-Spiegel. 1993. H.1, S.56-63

Theme

Semantisches Umfeld in Indexierung u. Retrieval

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Source

Grundlagen der praktischen Information und Dokumentation. Handbuch zur Einführung in die Informationswissenschaft und -praxis. 6., völlig neu gefaßte Ausgabe. Hrsg. von R. Kuhlen, W. Semar u. D. Strauch. Begründet von Klaus Laisiepen, Ernst Lutterbeck, Karl-Heinrich Meyer-Uhlenried

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Abstract

With the terrific growth of data volume and data being produced every second on millions of devices across the globe, there is a desperate need to manage the unstructured data available on web pages efficiently. Semantic Web or also known as Web of Trust structures the scattered data on the Internet according to the needs of the user. It is an extension of the World Wide Web (WWW) which focuses on manipulating web data on behalf of Humans. Due to the ability of the Semantic Web to integrate data from disparate sources and hence makes it more user-friendly, it is an emerging trend. Tim Berners-Lee first introduced the term Semantic Web and since then it has come a long way to become a more intelligent and intuitive web. Data Visualization plays an essential role in explaining complex concepts in a universal manner through pictorial representation, and the Semantic Web helps in broadening the potential of Data Visualization and thus making it an appropriate combination. The objective of this chapter is to provide fundamental insights concerning the semantic web technologies and in addition to that it also elucidates the issues as well as the solutions regarding the semantic web. The purpose of this chapter is to highlight the semantic web architecture in detail while also comparing it with the traditional search system. It classifies the semantic web architecture into three major pillars i.e. RDF, Ontology, and XML. Moreover, it describes different semantic web tools used in the framework and technology. It attempts to illustrate different approaches of the semantic web search engines. Besides stating numerous challenges faced by the semantic web it also illustrates the solutions.

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Abstract

Das Themengebiet der Arbeit behandelt Visualisierungen von typisierten Links in Linked Data. Die wissenschaftlichen Gebiete, die im Allgemeinen den Inhalt des Beitrags abgrenzen, sind das Semantic Web, das Web of Data und Informationsvisualisierung. Das Semantic Web, das von Tim Berners Lee 2001 erfunden wurde, stellt eine Erweiterung zum World Wide Web (Web 2.0) dar. Aktuelle Forschungen beziehen sich auf die Verknüpfbarkeit von Informationen im World Wide Web. Um es zu ermöglichen, solche Verbindungen wahrnehmen und verarbeiten zu können sind Visualisierungen die wichtigsten Anforderungen als Hauptteil der Datenverarbeitung. Im Zusammenhang mit dem Sematic Web werden Repräsentationen von zusammenhängenden Informationen anhand von Graphen gehandhabt. Der Grund des Entstehens dieser Arbeit ist in erster Linie die Beschreibung der Gestaltung von Linked Data-Visualisierungskonzepten, deren Prinzipien im Rahmen einer theoretischen Annäherung eingeführt werden. Anhand des Kontexts führt eine schrittweise Erweiterung der Informationen mit dem Ziel, praktische Richtlinien anzubieten, zur Vernetzung dieser ausgearbeiteten Gestaltungsrichtlinien. Indem die Entwürfe zweier alternativer Visualisierungen einer standardisierten Webapplikation beschrieben werden, die Linked Data als Netzwerk visualisiert, konnte ein Test durchgeführt werden, der deren Kompatibilität zum Inhalt hatte. Der praktische Teil behandelt daher die Designphase, die Resultate, und zukünftige Anforderungen des Projektes, die durch die Testung ausgearbeitet wurden.

Source

Mitteilungen der Vereinigung Österreichischer Bibliothekarinnen und Bibliothekare. 70(2017) H.2, S.179-199

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Date

22. 3.2008 14:35:21

Issue

2., überarb. u. erw. Aufl.

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Date

23. 3.2008 19:10:22

Editor

Fayyad, U. et al.

Footnote

Rez. in: JASIST 54(2003) no.9, S.905-906 (C.A. Badurek): "Visual approaches for knowledge discovery in very large databases are a prime research need for information scientists focused an extracting meaningful information from the ever growing stores of data from a variety of domains, including business, the geosciences, and satellite and medical imagery. This work presents a summary of research efforts in the fields of data mining, knowledge discovery, and data visualization with the goal of aiding the integration of research approaches and techniques from these major fields. The editors, leading computer scientists from academia and industry, present a collection of 32 papers from contributors who are incorporating visualization and data mining techniques through academic research as well application development in industry and government agencies. Information Visualization focuses upon techniques to enhance the natural abilities of humans to visually understand data, in particular, large-scale data sets. It is primarily concerned with developing interactive graphical representations to enable users to more intuitively make sense of multidimensional data as part of the data exploration process. It includes research from computer science, psychology, human-computer interaction, statistics, and information science. Knowledge Discovery in Databases (KDD) most often refers to the process of mining databases for previously unknown patterns and trends in data. Data mining refers to the particular computational methods or algorithms used in this process. The data mining research field is most related to computational advances in database theory, artificial intelligence and machine learning. This work compiles research summaries from these main research areas in order to provide "a reference work containing the collection of thoughts and ideas of noted researchers from the fields of data mining and data visualization" (p. 8). It addresses these areas in three main sections: the first an data visualization, the second an KDD and model visualization, and the last an using visualization in the knowledge discovery process. The seven chapters of Part One focus upon methodologies and successful techniques from the field of Data Visualization. Hoffman and Grinstein (Chapter 2) give a particularly good overview of the field of data visualization and its potential application to data mining. An introduction to the terminology of data visualization, relation to perceptual and cognitive science, and discussion of the major visualization display techniques are presented. Discussion and illustration explain the usefulness and proper context of such data visualization techniques as scatter plots, 2D and 3D isosurfaces, glyphs, parallel coordinates, and radial coordinate visualizations. Remaining chapters present the need for standardization of visualization methods, discussion of user requirements in the development of tools, and examples of using information visualization in addressing research problems.
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.

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Source

Interactive information seeking, behaviour and retrieval. Eds.: Ruthven, I. u. D. Kelly

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Source

Wa(h)re Information: 29. Österreichischer Bibliothekartag Bregenz, 19.-23.9.2006. Hrsg.: Harald Weigel

Theme

Semantisches Umfeld in Indexierung u. Retrieval

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Theme

Semantisches Umfeld in Indexierung u. Retrieval

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Source

Empirical foundation of information and software science. Ed.: J.C. Agarwal u. P. Zunde

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Source

Linked data and user interaction: the road ahead. Eds.: Cervone, H.F. u. L.G. Svensson

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Source

Linked data and user interaction: the road ahead. Eds.: Cervone, H.F. u. L.G. Svensson

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Tendencias de investigación en organización del conocimient: IV Cologuio International de Ciencas de la Documentación , VI Congreso del Capitulo Espanol de ISKO = Trends in knowledge organization research. Eds.: J.A. Frias u. C. Travieso

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¬La interdisciplinariedad y la transdisciplinariedad en la organización del conocimiento científico : actas del VIII Congreso ISKO-España, León, 18, 19 y 20 de Abril de 2007 : Interdisciplinarity and transdisciplinarity in the organization of scientific knowledge. Ed.: B. Rodriguez Bravo u. M.L Alvite Diez

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Source

Re:inventing information science in the networked society: Proceedings of the 14th International Symposium on Information Science, Zadar/Croatia, 19th-21st May 2015. Eds.: F. Pehar, C. Schloegl u. C. Wolff

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Footnote

Rez. in: JASIST 61(2010) no.4, S. 853-854 (L. Wilkinson)

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Editor

Munzner, T. u. S. North

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Source

¬La interdisciplinariedad y la transdisciplinariedad en la organización del conocimiento científico : actas del VIII Congreso ISKO-España, León, 18, 19 y 20 de Abril de 2007 : Interdisciplinarity and transdisciplinarity in the organization of scientific knowledge. Ed.: B. Rodriguez Bravo u. M.L Alvite Diez

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Source

Social Semantic Web: Web 2.0, was nun? Hrsg.: A. Blumauer u. T. Pellegrini