Search (6 results, page 1 of 1)

0.029806487 = product of:
  0.059612975 = sum of:
    0.015353453 = weight(_text_:information in 4731) [ClassicSimilarity], result of:
      0.015353453 = score(doc=4731,freq=10.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.1734784 = fieldWeight in 4731, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.03125 = fieldNorm(doc=4731)
    0.044259522 = weight(_text_:standards in 4731) [ClassicSimilarity], result of:
      0.044259522 = score(doc=4731,freq=2.0), product of:
        0.22470023 = queryWeight, product of:
          4.4569545 = idf(docFreq=1393, maxDocs=44218)
          0.050415643 = queryNorm
        0.19697142 = fieldWeight in 4731, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4569545 = idf(docFreq=1393, maxDocs=44218)
          0.03125 = fieldNorm(doc=4731)
  0.5 = coord(2/4)

Abstract

Das Werk behandelt die aktuellen Entwicklungen zur inhaltlichen Erschließung von Informationsquellen im Internet. Topic Maps, semantische Modelle vernetzter Informationsressourcen unter Verwendung von XML bzw. HyTime, bieten alle notwendigen Modellierungskonstrukte, um Dokumente im Internet zu klassifizieren und ein assoziatives, semantisches Netzwerk über diese zu legen. Neben Einführungen in XML, XLink, XPointer sowie HyTime wird anhand von Einsatzszenarien gezeigt, wie diese neuartige Technologie für Content Management und Information Retrieval im Internet funktioniert. Der Entwurf einer Abfragesprache wird ebenso skizziert wie der Prototyp einer intelligenten Suchmaschine. Das Buch zeigt, wie Topic Maps den Weg zu semantisch gesteuerten Suchprozessen im Internet weisen.

Content

Topic Maps - Einführung in den ISO Standard (Topics, Associations, Scopes, Facets, Topic Maps).- Grundlagen von XML (Aufbau, Bestandteile, Element- und Attributdefinitionen, DTD, XLink, XPointer).- Wie entsteht ein Heringsschmaus? Konkretes Beispiel einer Topic Map.Topic Maps - Meta DTD. Die formale Beschreibung des Standards.- HyTime als zugrunde liegender Formalismus (Bounded Object Sets, Location Addressing, Hyperlinks in HyTime).- Prototyp eines Topic Map Repositories (Entwicklungsprozess für Topic Maps, Prototyp Spezifikation, technische Realisierung des Prototyps).- Semantisches Datenmodell zur Speicherung von Topic Maps.- Prototypische Abfragesprache für Topic Maps.- Erweiterungsvorschläge für den ISO Standard.

RSWK

Internet / Information Retrieval / Semantisches Netz / HyTime
Internet / Information Retrieval / Semantisches Netz / XML

Subject

Internet / Information Retrieval / Semantisches Netz / HyTime
Internet / Information Retrieval / Semantisches Netz / XML

0.027870432 = product of:
  0.055740863 = sum of:
    0.0166206 = weight(_text_:information in 636) [ClassicSimilarity], result of:
      0.0166206 = score(doc=636,freq=30.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.18779588 = fieldWeight in 636, product of:
          5.477226 = tf(freq=30.0), with freq of:
            30.0 = termFreq=30.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.01953125 = fieldNorm(doc=636)
    0.03912026 = weight(_text_:standards in 636) [ClassicSimilarity], result of:
      0.03912026 = score(doc=636,freq=4.0), product of:
        0.22470023 = queryWeight, product of:
          4.4569545 = idf(docFreq=1393, maxDocs=44218)
          0.050415643 = queryNorm
        0.17409979 = fieldWeight in 636, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.4569545 = idf(docFreq=1393, maxDocs=44218)
          0.01953125 = fieldNorm(doc=636)
  0.5 = coord(2/4)

Abstract

The Text REtrieval Conference (TREC), a yearly workshop hosted by the US government's National Institute of Standards and Technology, provides the infrastructure necessary for large-scale evaluation of text retrieval methodologies. With the goal of accelerating research in this area, TREC created the first large test collections of full-text documents and standardized retrieval evaluation. The impact has been significant; since TREC's beginning in 1992, retrieval effectiveness has approximately doubled. TREC has built a variety of large test collections, including collections for such specialized retrieval tasks as cross-language retrieval and retrieval of speech. Moreover, TREC has accelerated the transfer of research ideas into commercial systems, as demonstrated in the number of retrieval techniques developed in TREC that are now used in Web search engines. This book provides a comprehensive review of TREC research, summarizing the variety of TREC results, documenting the best practices in experimental information retrieval, and suggesting areas for further research. The first part of the book describes TREC's history, test collections, and retrieval methodology. Next, the book provides "track" reports -- describing the evaluations of specific tasks, including routing and filtering, interactive retrieval, and retrieving noisy text. The final part of the book offers perspectives on TREC from such participants as Microsoft Research, University of Massachusetts, Cornell University, University of Waterloo, City University of New York, and IBM. The book will be of interest to researchers in information retrieval and related technologies, including natural language processing.

Footnote

Rez. in: JASIST 58(2007) no.6, S.910-911 (J.L. Vicedo u. J. Gomez): "The Text REtrieval Conference (TREC) is a yearly workshop hosted by the U.S. government's National Institute of Standards and Technology (NIST) that fosters and supports research in information retrieval as well as speeding the transfer of technology between research labs and industry. Since 1992, TREC has provided the infrastructure necessary for large-scale evaluations of different text retrieval methodologies. TREC impact has been very important and its success has been mainly supported by its continuous adaptation to the emerging information retrieval needs. Not in vain, TREC has built evaluation benchmarks for more than 20 different retrieval problems such as Web retrieval, speech retrieval, or question-answering. The large and intense trajectory of annual TREC conferences has resulted in an immense bulk of documents reflecting the different eval uation and research efforts developed. This situation makes it difficult sometimes to observe clearly how research in information retrieval (IR) has evolved over the course of TREC. TREC: Experiment and Evaluation in Information Retrieval succeeds in organizing and condensing all this research into a manageable volume that describes TREC history and summarizes the main lessons learned. The book is organized into three parts. The first part is devoted to the description of TREC's origin and history, the test collections, and the evaluation methodology developed. The second part describes a selection of the major evaluation exercises (tracks), and the third part contains contributions from research groups that had a large and remarkable participation in TREC. Finally, Karen Spark Jones, one of the main promoters of research in IR, closes the book with an epilogue that analyzes the impact of TREC on this research field.
... TREC: Experiment and Evaluation in Information Retrieval is a reliable and comprehensive review of the TREC program and has been adopted by NIST as the official history of TREC (see http://trec.nist.gov). We were favorably surprised by the book. Well structured and written, chapters are self-contained and the existence of references to specialized and more detailed publications is continuous, which makes it easier to expand into the different aspects analyzed in the text. This book succeeds in compiling TREC evolution from its inception in 1992 to 2003 in an adequate and manageable volume. Thanks to the impressive effort performed by the authors and their experience in the field, it can satiate the interests of a great variety of readers. While expert researchers in the IR field and IR-related industrial companies can use it as a reference manual, it seems especially useful for students and non-expert readers willing to approach this research area. Like NIST, we would recommend this reading to anyone who may be interested in textual information retrieval."

LCSH

Information storage and retrieval systems / Congresses

RSWK

Information Retrieval / Textverarbeitung / Aufsatzsammlung (BVB)
Kongress / Information Retrieval / Kongress (GBV)

Subject

Information Retrieval / Textverarbeitung / Aufsatzsammlung (BVB)
Kongress / Information Retrieval / Kongress (GBV)
Information storage and retrieval systems / Congresses

0.008539819 = product of:
  0.034159277 = sum of:
    0.034159277 = weight(_text_:information in 2605) [ClassicSimilarity], result of:
      0.034159277 = score(doc=2605,freq=22.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.38596505 = fieldWeight in 2605, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=2605)
  0.25 = coord(1/4)

Abstract

For introductory information retrieval courses at the undergraduate and graduate level in computer science, information science and computer engineering departments. Written by a leader in the field of information retrieval, Search Engines: Information Retrieval in Practice, is designed to give undergraduate students the understanding and tools they need to evaluate, compare and modify search engines. Coverage of the underlying IR and mathematical models reinforce key concepts. The book's numerous programming exercises make extensive use of Galago, a Java-based open source search engine. SUPPLEMENTS / Extensive lecture slides (in PDF and PPT format) / Solutions to selected end of chapter problems (Instructors only) / Test collections for exercises / Galago search engine

LCSH

Information retrieval
Information Storage and Retrieval

RSWK

Suchmaschine / Information Retrieval

Subject

Suchmaschine / Information Retrieval
Information retrieval
Information Storage and Retrieval

0.008028513 = product of:
  0.03211405 = sum of:
    0.03211405 = weight(_text_:information in 262) [ClassicSimilarity], result of:
      0.03211405 = score(doc=262,freq=28.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.3628561 = fieldWeight in 262, product of:
          5.2915025 = tf(freq=28.0), with freq of:
            28.0 = termFreq=28.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=262)
  0.25 = coord(1/4)

Abstract

The Architecture of Cyberspace is aimed at students taking information management as a minor in their course as well as those who manage document collections but who are not professional librarians. The first part of this book looks at how users find documents and the problems they have; the second part discusses how to manage the information space using various tools such as classification and controlled vocabularies. It also explores the general issues of publishing, including legal considerations, as well the main issues of creating and managing archives. Supported by exercises and discussion questions at the end of each chapter, the book includes some sample assignments suitable for use with students of this subject. A glossary is also provided to help readers understand the specialised vocabulary and the key concepts in the design and assessment of information spaces.

LCSH

Information technology
Information retrieval
Information resources management

RSWK

Information Engineering / Lehrbuch
Information Retrieval / Lehrbuch

Subject

Information Engineering / Lehrbuch
Information Retrieval / Lehrbuch
Information technology
Information retrieval
Information resources management

0.005946367 = product of:
  0.023785468 = sum of:
    0.023785468 = weight(_text_:information in 26) [ClassicSimilarity], result of:
      0.023785468 = score(doc=26,freq=24.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.2687516 = fieldWeight in 26, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.03125 = fieldNorm(doc=26)
  0.25 = coord(1/4)

Abstract

We live in an information age that requires us, more than ever, to represent, access, and use information. Over the last several decades, we have developed a modern science and technology for information retrieval, relentlessly pursuing the vision of a "memex" that Vannevar Bush proposed in his seminal article, "As We May Think." Faceted search plays a key role in this program. Faceted search addresses weaknesses of conventional search approaches and has emerged as a foundation for interactive information retrieval. User studies demonstrate that faceted search provides more effective information-seeking support to users than best-first search. Indeed, faceted search has become increasingly prevalent in online information access systems, particularly for e-commerce and site search. In this lecture, we explore the history, theory, and practice of faceted search. Although we cannot hope to be exhaustive, our aim is to provide sufficient depth and breadth to offer a useful resource to both researchers and practitioners. Because faceted search is an area of interest to computer scientists, information scientists, interface designers, and usability researchers, we do not assume that the reader is a specialist in any of these fields. Rather, we offer a self-contained treatment of the topic, with an extensive bibliography for those who would like to pursue particular aspects in more depth.

Content

Table of Contents: I. Key Concepts / Introduction: What Are Facets? / Information Retrieval / Faceted Information Retrieval / II. Research and Practice / Academic Research / Commercial Applications / III. Practical Concerns / Back-End Concerns / Front-End Concerns / Conclusion / Glossary

RSWK

Information Retrieval

Series

Synthesis lectures on information concepts, retrieval & services; 5

Subject

Information Retrieval

0.005757545 = product of:
  0.02303018 = sum of:
    0.02303018 = weight(_text_:information in 4332) [ClassicSimilarity], result of:
      0.02303018 = score(doc=4332,freq=10.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.2602176 = fieldWeight in 4332, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=4332)
  0.25 = coord(1/4)

Abstract

Das Semantic Web bezeichnet ein erweitertes World Wide Web (WWW), das die Bedeutung von präsentierten Inhalten in neuen standardisierten Sprachen wie RDF Schema und OWL modelliert. Diese Arbeit befasst sich mit dem Aspekt des Information Retrieval, d.h. es wird untersucht, in wie weit Methoden der Informationssuche sich auf modelliertes Wissen übertragen lassen. Die kennzeichnenden Merkmale von IR-Systemen wie vage Anfragen sowie die Unterstützung unsicheren Wissens werden im Kontext des Semantic Web behandelt. Im Fokus steht die Suche nach Fakten innerhalb einer Wissensdomäne, die entweder explizit modelliert sind oder implizit durch die Anwendung von Inferenz abgeleitet werden können. Aufbauend auf der an der Universität Duisburg-Essen entwickelten Retrievalmaschine PIRE wird die Anwendung unsicherer Inferenz mit probabilistischer Prädikatenlogik (pDatalog) implementiert.

Footnote

Zugl.: Dortmund, Univ., Dipl.-Arb., 2006 u.d.T.: Hüsken, Peter: Information-Retrieval im Semantic-Web.

RSWK

Information Retrieval / Semantic Web

Subject

Information Retrieval / Semantic Web