Search (3 results, page 1 of 1)

0.021799784 = product of:
  0.08719914 = sum of:
    0.024209278 = weight(_text_:und in 6043) [ClassicSimilarity], result of:
      0.024209278 = score(doc=6043,freq=30.0), product of:
        0.06381599 = queryWeight, product of:
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.02879306 = queryNorm
        0.3793607 = fieldWeight in 6043, product of:
          5.477226 = tf(freq=30.0), with freq of:
            30.0 = termFreq=30.0
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.03125 = fieldNorm(doc=6043)
    0.02375705 = weight(_text_:der in 6043) [ClassicSimilarity], result of:
      0.02375705 = score(doc=6043,freq=28.0), product of:
        0.06431698 = queryWeight, product of:
          2.2337668 = idf(docFreq=12875, maxDocs=44218)
          0.02879306 = queryNorm
        0.36937445 = fieldWeight in 6043, product of:
          5.2915025 = tf(freq=28.0), with freq of:
            28.0 = termFreq=28.0
          2.2337668 = idf(docFreq=12875, maxDocs=44218)
          0.03125 = fieldNorm(doc=6043)
    0.024209278 = weight(_text_:und in 6043) [ClassicSimilarity], result of:
      0.024209278 = score(doc=6043,freq=30.0), product of:
        0.06381599 = queryWeight, product of:
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.02879306 = queryNorm
        0.3793607 = fieldWeight in 6043, product of:
          5.477226 = tf(freq=30.0), with freq of:
            30.0 = termFreq=30.0
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.03125 = fieldNorm(doc=6043)
    0.009758773 = weight(_text_:des in 6043) [ClassicSimilarity], result of:
      0.009758773 = score(doc=6043,freq=2.0), product of:
        0.079736836 = queryWeight, product of:
          2.7693076 = idf(docFreq=7536, maxDocs=44218)
          0.02879306 = queryNorm
        0.12238726 = fieldWeight in 6043, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.7693076 = idf(docFreq=7536, maxDocs=44218)
          0.03125 = fieldNorm(doc=6043)
    0.0052647553 = weight(_text_:in in 6043) [ClassicSimilarity], result of:
      0.0052647553 = score(doc=6043,freq=10.0), product of:
        0.039165888 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.02879306 = queryNorm
        0.13442196 = fieldWeight in 6043, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.03125 = fieldNorm(doc=6043)
  0.25 = coord(5/20)

Abstract

Die Süddeutsche Zeitung (SZ) verfügt seit ihrer Gründung 1945 über ein Pressearchiv, das die Texte der eigenen Redakteure und zahlreicher nationaler und internationaler Publikationen dokumentiert und für Recherchezwecke bereitstellt. Die DIZ-Pressedatenbank (www.medienport.de) ermöglicht die browserbasierte Recherche für Redakteure und externe Kunden im Intra- und Internet und die kundenspezifischen Content Feeds für Verlage, Rundfunkanstalten und Portale. Die DIZ-Pressedatenbank enthält z. Zt. 7,8 Millionen Artikel, die jeweils als HTML oder PDF abrufbar sind. Täglich kommen ca. 3.500 Artikel hinzu, von denen ca. 1.000 durch Dokumentare inhaltlich erschlossen werden. Die Informationserschließung erfolgt im DIZ nicht durch die Vergabe von Schlagwörtern am Dokument, sondern durch die Verlinkung der Artikel mit "virtuellen Mappen", den Dossiers. Insgesamt enthält die DIZ-Pressedatenbank ca. 90.000 Dossiers, die untereinander zum "DIZ-Wissensnetz" verlinkt sind. DIZ definiert das Wissensnetz als Alleinstellungsmerkmal und wendet beträchtliche personelle Ressourcen für die Aktualisierung und Qualitätssicherung der Dossiers auf. Im Zuge der Medienkrise mussten sich DIZ der Herausforderung stellen, bei sinkenden Lektoratskapazitäten die Qualität der Informationserschließung im Input zu erhalten. Auf der Outputseite gilt es, eine anspruchsvolle Zielgruppe - u.a. die Redakteure der Süddeutschen Zeitung - passgenau und zeitnah mit den Informationen zu versorgen, die sie für ihre tägliche Arbeit benötigt. Bezogen auf die Ausgangssituation in der Dokumentation der Süddeutschen Zeitung identifizierte DIZ drei Ansatzpunkte, wie die Aufwände auf der Inputseite (Lektorat) zu optimieren sind und gleichzeitig auf der Outputseite (Recherche) das Wissensnetz besser zu vermarkten ist: - (Teil-)Automatische Klassifizierung von Pressetexten (Vorschlagwesen) - Visualisierung des Wissensnetzes - Neue Retrievalmöglichkeiten (Ähnlichkeitssuche, Clustering) Im Bereich "Visualisierung" setzt DIZ auf den Net-Navigator von intelligent views, eine interaktive Visualisierung allgemeiner Graphen, basierend auf einem physikalischen Modell. In den Bereichen automatische Klassifizierung, Ähnlichkeitssuche und Clustering hat DIZ sich für das Produkt nextBot der Firma Brainbot entschieden.

Source

Spezialbibliotheken zwischen Auftrag und Ressourcen: 6.-9. September 2005 in München, 30. Arbeits- und Fortbildungstagung der ASpB e.V. / Sektion 5 im Deutschen Bibliotheksverband. Red.: M. Brauer

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.018718263 = product of:
  0.07487305 = sum of:
    0.015627023 = weight(_text_:und in 566) [ClassicSimilarity], result of:
      0.015627023 = score(doc=566,freq=8.0), product of:
        0.06381599 = queryWeight, product of:
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.02879306 = queryNorm
        0.24487628 = fieldWeight in 566, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.0390625 = fieldNorm(doc=566)
    0.026322968 = weight(_text_:der in 566) [ClassicSimilarity], result of:
      0.026322968 = score(doc=566,freq=22.0), product of:
        0.06431698 = queryWeight, product of:
          2.2337668 = idf(docFreq=12875, maxDocs=44218)
          0.02879306 = queryNorm
        0.40926933 = fieldWeight in 566, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          2.2337668 = idf(docFreq=12875, maxDocs=44218)
          0.0390625 = fieldNorm(doc=566)
    0.015627023 = weight(_text_:und in 566) [ClassicSimilarity], result of:
      0.015627023 = score(doc=566,freq=8.0), product of:
        0.06381599 = queryWeight, product of:
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.02879306 = queryNorm
        0.24487628 = fieldWeight in 566, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.0390625 = fieldNorm(doc=566)
    0.012198467 = weight(_text_:des in 566) [ClassicSimilarity], result of:
      0.012198467 = score(doc=566,freq=2.0), product of:
        0.079736836 = queryWeight, product of:
          2.7693076 = idf(docFreq=7536, maxDocs=44218)
          0.02879306 = queryNorm
        0.15298408 = fieldWeight in 566, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.7693076 = idf(docFreq=7536, maxDocs=44218)
          0.0390625 = fieldNorm(doc=566)
    0.005097578 = weight(_text_:in in 566) [ClassicSimilarity], result of:
      0.005097578 = score(doc=566,freq=6.0), product of:
        0.039165888 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.02879306 = queryNorm
        0.1301535 = fieldWeight in 566, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=566)
  0.25 = coord(5/20)

Abstract

Eine der größten Herausforderungen für alle technischen Anwendungen ist die sogenannte Mensch-Maschine-Schnittstelle, also der Problemkreis, wie der bedienende Mensch mit der zu bedienenden Technik kommunizieren kann. Waren die Benutzungsschnittstellen bis Ende der Achtziger Jahre vor allem durch die Notwendigkeit des Benutzers geprägt, sich an die Erfordernisse der Maschine anzupassen, so wurde mit Durchsetzung grafischer Benutzungsoberflächen zunehmend versucht, die Bedienbarkeit so zu gestalten, dass ein Mensch auch ohne größere Einarbeitung in die Lage versetzt werden sollte, seine Befehle der Technik - letztlich also dem Computer - zu übermitteln. Trotz aller Fortschritte auf diesem Gebiet blieb immer die Anforderung, der Mensch solle auf die ihm natürlichste Art und Weise kommunizieren können, mit menschlicher Sprache. Diese Anforderung gilt gerade auch für das Retrieval von Informationen: Warum ist es nötig, die Nutzung von Booleschen Operatoren zu erlernen, nur um eine Suchanfrage stellen zu können? Ein anderes Thema ist die Frage nach der Visualisierung von Wissenszusammenhängen, die sich der Herausforderung stellt, in einem geradezu uferlos sich ausweitenden Informationsangebot weiterhin den Überblick behalten und relevante Informationen schnellstmöglich finden zu können.

Series

Schriften der Vereinigung Österreichischer Bibliothekarinnen und Bibliothekare (VÖB); Bd. 2

Theme

Semantisches Umfeld in Indexierung u. Retrieval

3.290472E-4 = product of:
  0.006580944 = sum of:
    0.006580944 = weight(_text_:in in 1211) [ClassicSimilarity], result of:
      0.006580944 = score(doc=1211,freq=40.0), product of:
        0.039165888 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.02879306 = queryNorm
        0.16802745 = fieldWeight in 1211, product of:
          6.3245554 = tf(freq=40.0), with freq of:
            40.0 = termFreq=40.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.01953125 = fieldNorm(doc=1211)
  0.05 = coord(1/20)

Abstract

In this article we present a method for retrieving documents from a digital library through a visual interface based on automatically generated concepts. We used a vocabulary generation algorithm to generate a set of concepts for the digital library and a technique called the max-min distance technique to cluster them. Additionally, the concepts were visualized in a spring embedding graph layout to depict the semantic relationship among them. The resulting graph layout serves as an aid to users for retrieving documents. An online archive containing the contents of D-Lib Magazine from July 1995 to May 2002 was used to test the utility of an implemented retrieval and visualization system. We believe that the method developed and tested can be applied to many different domains to help users get a better understanding of online document collections and to minimize users' cognitive load during execution of search tasks. Over the past few years, the volume of information available through the World Wide Web has been expanding exponentially. Never has so much information been so readily available and shared among so many people. Unfortunately, the unstructured nature and huge volume of information accessible over networks have made it hard for users to sift through and find relevant information. To deal with this problem, information retrieval (IR) techniques have gained more intensive attention from both industrial and academic researchers. Numerous IR techniques have been developed to help deal with the information overload problem. These techniques concentrate on mathematical models and algorithms for retrieval. Popular IR models such as the Boolean model, the vector-space model, the probabilistic model and their variants are well established.
From the user's perspective, however, it is still difficult to use current information retrieval systems. Users frequently have problems expressing their information needs and translating those needs into queries. This is partly due to the fact that information needs cannot be expressed appropriately in systems terms. It is not unusual for users to input search terms that are different from the index terms information systems use. Various methods have been proposed to help users choose search terms and articulate queries. One widely used approach is to incorporate into the information system a thesaurus-like component that represents both the important concepts in a particular subject area and the semantic relationships among those concepts. Unfortunately, the development and use of thesauri is not without its own problems. The thesaurus employed in a specific information system has often been developed for a general subject area and needs significant enhancement to be tailored to the information system where it is to be used. This thesaurus development process, if done manually, is both time consuming and labor intensive. Usage of a thesaurus in searching is complex and may raise barriers for the user. For illustration purposes, let us consider two scenarios of thesaurus usage. In the first scenario the user inputs a search term and the thesaurus then displays a matching set of related terms. Without an overview of the thesaurus - and without the ability to see the matching terms in the context of other terms - it may be difficult to assess the quality of the related terms in order to select the correct term. In the second scenario the user browses the whole thesaurus, which is organized as in an alphabetically ordered list. The problem with this approach is that the list may be long, and neither does it show users the global semantic relationship among all the listed terms.
Nevertheless, because thesaurus use has shown to improve retrieval, for our method we integrate functions in the search interface that permit users to explore built-in search vocabularies to improve retrieval from digital libraries. Our method automatically generates the terms and their semantic relationships representing relevant topics covered in a digital library. We call these generated terms the "concepts", and the generated terms and their semantic relationships we call the "concept space". Additionally, we used a visualization technique to display the concept space and allow users to interact with this space. The automatically generated term set is considered to be more representative of subject area in a corpus than an "externally" imposed thesaurus, and our method has the potential of saving a significant amount of time and labor for those who have been manually creating thesauri as well. Information visualization is an emerging discipline and developed very quickly in the last decade. With growing volumes of documents and associated complexities, information visualization has become increasingly important. Researchers have found information visualization to be an effective way to use and understand information while minimizing a user's cognitive load. Our work was based on an algorithmic approach of concept discovery and association. Concepts are discovered using an algorithm based on an automated thesaurus generation procedure. Subsequently, similarities among terms are computed using the cosine measure, and the associations among terms are established using a method known as max-min distance clustering. The concept space is then visualized in a spring embedding graph, which roughly shows the semantic relationships among concepts in a 2-D visual representation. The semantic space of the visualization is used as a medium for users to retrieve the desired documents. In the remainder of this article, we present our algorithmic approach of concept generation and clustering, followed by description of the visualization technique and interactive interface. The paper ends with key conclusions and discussions on future work.

Theme

Semantisches Umfeld in Indexierung u. Retrieval