Search (11 results, page 1 of 1)

  • × year_i:[2020 TO 2030}
  • × theme_ss:"Suchmaschinen"
  1. Lewandowski, D.: Suchmaschinen verstehen : 3. vollständig überarbeitete und erweiterte Aufl. (2021) 0.01
    0.010015186 = product of:
      0.0701063 = sum of:
        0.045449268 = weight(_text_:wide in 4016) [ClassicSimilarity], result of:
          0.045449268 = score(doc=4016,freq=4.0), product of:
            0.1312982 = queryWeight, product of:
              4.4307585 = idf(docFreq=1430, maxDocs=44218)
              0.029633347 = queryNorm
            0.34615302 = fieldWeight in 4016, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.4307585 = idf(docFreq=1430, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4016)
        0.02465703 = weight(_text_:web in 4016) [ClassicSimilarity], result of:
          0.02465703 = score(doc=4016,freq=4.0), product of:
            0.09670874 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.029633347 = queryNorm
            0.25496176 = fieldWeight in 4016, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4016)
      0.14285715 = coord(2/14)
    
    RSWK
    World Wide Web Recherche
    Subject
    World Wide Web Recherche
  2. Lewandowski, D.: Suchmaschinen (2023) 0.01
    0.009736202 = product of:
      0.06815341 = sum of:
        0.03856498 = weight(_text_:wide in 793) [ClassicSimilarity], result of:
          0.03856498 = score(doc=793,freq=2.0), product of:
            0.1312982 = queryWeight, product of:
              4.4307585 = idf(docFreq=1430, maxDocs=44218)
              0.029633347 = queryNorm
            0.29372054 = fieldWeight in 793, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4307585 = idf(docFreq=1430, maxDocs=44218)
              0.046875 = fieldNorm(doc=793)
        0.029588435 = weight(_text_:web in 793) [ClassicSimilarity], result of:
          0.029588435 = score(doc=793,freq=4.0), product of:
            0.09670874 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.029633347 = queryNorm
            0.3059541 = fieldWeight in 793, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.046875 = fieldNorm(doc=793)
      0.14285715 = coord(2/14)
    
    Abstract
    Eine Suchmaschine (auch: Web-Suchmaschine, Universalsuchmaschine) ist ein Computersystem, das Inhalte aus dem World Wide Web (WWW) mittels Crawling erfasst und über eine Benutzerschnittstelle durchsuchbar macht, wobei die Ergebnisse in einer nach systemseitig angenommener Relevanz geordneten Darstellung aufgeführt werden. Dies bedeutet, dass Suchmaschinen im Gegensatz zu anderen Informationssystemen nicht auf einem klar abgegrenzten Datenbestand aufbauen, sondern diesen aus den verstreut vorliegenden Dokumenten des WWW zusammenstellen. Dieser Datenbestand wird über eine Benutzerschnittstelle zugänglich gemacht, die so gestaltet ist, dass die Suchmaschine von Laien problemlos genutzt werden kann. Die zu einer Suchanfrage ausgegebenen Treffer werden so sortiert, dass den Nutzenden die aus Systemsicht relevantesten Dokumente zuerst angezeigt werden. Dabei handelt es sich um komplexe Bewertungsverfahren, denen zahlreiche Annahmen über die Relevanz von Dokumenten in Bezug auf Suchanfragen zugrunde liegen.
  3. Zeynali-Tazehkandi, M.; Nowkarizi, M.: ¬ A dialectical approach to search engine evaluation (2020) 0.01
    0.0061772587 = product of:
      0.043240808 = sum of:
        0.012107591 = weight(_text_:information in 185) [ClassicSimilarity], result of:
          0.012107591 = score(doc=185,freq=8.0), product of:
            0.052020688 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.029633347 = queryNorm
            0.23274569 = fieldWeight in 185, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=185)
        0.031133216 = weight(_text_:retrieval in 185) [ClassicSimilarity], result of:
          0.031133216 = score(doc=185,freq=6.0), product of:
            0.08963835 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.029633347 = queryNorm
            0.34732026 = fieldWeight in 185, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.046875 = fieldNorm(doc=185)
      0.14285715 = coord(2/14)
    
    Abstract
    Evaluation of information retrieval systems is a fundamental topic in Library and Information Science. The aim of this paper is to connect the system-oriented and the user-oriented approaches to relevant philosophical schools. By reviewing the related literature, it was found that the evaluation of information retrieval systems is successful if it benefits from both system-oriented and user-oriented approaches (composite). The system-oriented approach is rooted in Parmenides' philosophy of stability (immovable) which Plato accepts and attributes to the world of forms; the user-oriented approach is rooted in Heraclitus' flux philosophy (motion) which Plato defers and attributes to the tangible world. Thus, using Plato's theory is a comprehensive approach for recognizing the concept of relevance. The theoretical and philosophical foundations determine the type of research methods and techniques. Therefore, Plato's dialectical method is an appropriate composite method for evaluating information retrieval systems.
  4. Sundin, O.; Lewandowski, D.; Haider, J.: Whose relevance? : Web search engines as multisided relevance machines (2022) 0.01
    0.005234611 = product of:
      0.036642276 = sum of:
        0.024409214 = weight(_text_:web in 542) [ClassicSimilarity], result of:
          0.024409214 = score(doc=542,freq=2.0), product of:
            0.09670874 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.029633347 = queryNorm
            0.25239927 = fieldWeight in 542, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0546875 = fieldNorm(doc=542)
        0.012233062 = weight(_text_:information in 542) [ClassicSimilarity], result of:
          0.012233062 = score(doc=542,freq=6.0), product of:
            0.052020688 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.029633347 = queryNorm
            0.23515764 = fieldWeight in 542, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0546875 = fieldNorm(doc=542)
      0.14285715 = coord(2/14)
    
    Abstract
    This opinion piece takes Google's response to the so-called COVID-19 infodemic, as a starting point to argue for the need to consider societal relevance as a complement to other types of relevance. The authors maintain that if information science wants to be a discipline at the forefront of research on relevance, search engines, and their use, then the information science research community needs to address itself to the challenges and conditions that commercial search engines create in. The article concludes with a tentative list of related research topics.
    Source
    Journal of the Association for Information Science and Technology. 73(2022) no.5, S.637-642
  5. Kang, X.; Wu, Y.; Ren, W.: Toward action comprehension for searching : mining actionable intents in query entities (2020) 0.00
    0.0035812336 = product of:
      0.025068633 = sum of:
        0.010089659 = weight(_text_:information in 5613) [ClassicSimilarity], result of:
          0.010089659 = score(doc=5613,freq=8.0), product of:
            0.052020688 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.029633347 = queryNorm
            0.19395474 = fieldWeight in 5613, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5613)
        0.014978974 = weight(_text_:retrieval in 5613) [ClassicSimilarity], result of:
          0.014978974 = score(doc=5613,freq=2.0), product of:
            0.08963835 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.029633347 = queryNorm
            0.16710453 = fieldWeight in 5613, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5613)
      0.14285715 = coord(2/14)
    
    Abstract
    Understanding search engine users' intents has been a popular study in information retrieval, which directly affects the quality of retrieved information. One of the fundamental problems in this field is to find a connection between the entity in a query and the potential intents of the users, the latter of which would further reveal important information for facilitating the users' future actions. In this article, we present a novel research method for mining the actionable intents for search users, by generating a ranked list of the potentially most informative actions based on a massive pool of action samples. We compare different search strategies and their combinations for retrieving the action pool and develop three criteria for measuring the informativeness of the selected action samples, that is, the significance of an action sample within the pool, the representativeness of an action sample for the other candidate samples, and the diverseness of an action sample with respect to the selected actions. Our experiment, based on the Action Mining (AM) query entity data set from the Actionable Knowledge Graph (AKG) task at NTCIR-13, suggests that the proposed approach is effective in generating an informative and early-satisfying ranking of potential actions for search users.
    Source
    Journal of the Association for Information Science and Technology. 71(2020) no.2, S.143-157
  6. Vegt, A. van der; Zuccon, G.; Koopman, B.: Do better search engines really equate to better clinical decisions? : If not, why not? (2021) 0.00
    0.0033881254 = product of:
      0.023716876 = sum of:
        0.008737902 = weight(_text_:information in 150) [ClassicSimilarity], result of:
          0.008737902 = score(doc=150,freq=6.0), product of:
            0.052020688 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.029633347 = queryNorm
            0.16796975 = fieldWeight in 150, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=150)
        0.014978974 = weight(_text_:retrieval in 150) [ClassicSimilarity], result of:
          0.014978974 = score(doc=150,freq=2.0), product of:
            0.08963835 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.029633347 = queryNorm
            0.16710453 = fieldWeight in 150, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=150)
      0.14285715 = coord(2/14)
    
    Abstract
    Previous research has found that improved search engine effectiveness-evaluated using a batch-style approach-does not always translate to significant improvements in user task performance; however, these prior studies focused on simple recall and precision-based search tasks. We investigated the same relationship, but for realistic, complex search tasks required in clinical decision making. One hundred and nine clinicians and final year medical students answered 16 clinical questions. Although the search engine did improve answer accuracy by 20 percentage points, there was no significant difference when participants used a more effective, state-of-the-art search engine. We also found that the search engine effectiveness difference, identified in the lab, was diminished by around 70% when the search engines were used with real users. Despite the aid of the search engine, half of the clinical questions were answered incorrectly. We further identified the relative contribution of search engine effectiveness to the overall end task success. We found that the ability to interpret documents correctly was a much more important factor impacting task success. If these findings are representative, information retrieval research may need to reorient its emphasis towards helping users to better understand information, rather than just finding it for them.
    Source
    Journal of the Association for Information Science and Technology. 72(2021) no.2, S.141-155
  7. Advanced online media use (2023) 0.00
    0.0028179463 = product of:
      0.039451245 = sum of:
        0.039451245 = weight(_text_:web in 954) [ClassicSimilarity], result of:
          0.039451245 = score(doc=954,freq=4.0), product of:
            0.09670874 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.029633347 = queryNorm
            0.4079388 = fieldWeight in 954, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0625 = fieldNorm(doc=954)
      0.071428575 = coord(1/14)
    
    Content
    "1. Use a range of different media 2. Access paywalled media content 3. Use an advertising and tracking blocker 4. Use alternatives to Google Search 5. Use alternatives to YouTube 6. Use alternatives to Facebook and Twitter 7. Caution with Wikipedia 8. Web browser, email, and internet access 9. Access books and scientific papers 10. Access deleted web content"
  8. Ogden, J.; Summers, E.; Walker, S.: Know(ing) Infrastructure : the wayback machine as object and instrument of digital research (2023) 0.00
    0.002490736 = product of:
      0.034870304 = sum of:
        0.034870304 = weight(_text_:web in 1084) [ClassicSimilarity], result of:
          0.034870304 = score(doc=1084,freq=8.0), product of:
            0.09670874 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.029633347 = queryNorm
            0.36057037 = fieldWeight in 1084, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1084)
      0.071428575 = coord(1/14)
    
    Abstract
    From documenting human rights abuses to studying online advertising, web archives are increasingly positioned as critical resources for a broad range of scholarly Internet research agendas. In this article, we reflect on the motivations and methodological challenges of investigating the world's largest web archive, the Internet Archive's Wayback Machine (IAWM). Using a mixed methods approach, we report on a pilot project centred around documenting the inner workings of 'Save Page Now' (SPN) - an Internet Archive tool that allows users to initiate the creation and storage of 'snapshots' of web resources. By improving our understanding of SPN and its role in shaping the IAWM, this work examines how the public tool is being used to 'save the Web' and highlights the challenges of operationalising a study of the dynamic sociotechnical processes supporting this knowledge infrastructure. Inspired by existing Science and Technology Studies (STS) approaches, the paper charts our development of methodological interventions to support an interdisciplinary investigation of SPN, including: ethnographic methods, 'experimental blackbox tactics', data tracing, modelling and documentary research. We discuss the opportunities and limitations of our methodology when interfacing with issues associated with temporality, scale and visibility, as well as critically engage with our own positionality in the research process (in terms of expertise and access). We conclude with reflections on the implications of digital STS approaches for 'knowing infrastructure', where the use of these infrastructures is unavoidably intertwined with our ability to study the situated and material arrangements of their creation.
  9. Christensen, A.: Wissenschaftliche Literatur entdecken : was bibliothekarische Discovery-Systeme von der Konkurrenz lernen und was sie ihr zeigen können (2022) 0.00
    0.0017435154 = product of:
      0.024409214 = sum of:
        0.024409214 = weight(_text_:web in 833) [ClassicSimilarity], result of:
          0.024409214 = score(doc=833,freq=2.0), product of:
            0.09670874 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.029633347 = queryNorm
            0.25239927 = fieldWeight in 833, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0546875 = fieldNorm(doc=833)
      0.071428575 = coord(1/14)
    
    Abstract
    In den letzten Jahren ist das Angebot an Academic Search Engines für die Recherche nach Fachliteratur zu allen Wissenschaftsgebieten stark angewachsen und ergänzt die beliebten kommerziellen Angebote wie Web of Science oder Scopus. Der Artikel zeigt die wesentlichen Unterschiede zwischen bibliothekarischen Discovery-Systemen und Academic Search Engines wie Base, Dimensions oder Open Alex auf und diskutiert Möglichkeiten, wie beide von einander profitieren können. Diese Entwicklungsperspektiven betreffen Aspekte wie die Kontextualisierung von Wissen, die Datenmodellierung, die automatischen Datenanreicherung sowie den Zuschnitt von Suchräumen.
  10. Weiß, E.-M.: ChatGPT soll es richten : Microsoft baut KI in Suchmaschine Bing ein (2023) 0.00
    5.04483E-4 = product of:
      0.0070627616 = sum of:
        0.0070627616 = weight(_text_:information in 866) [ClassicSimilarity], result of:
          0.0070627616 = score(doc=866,freq=2.0), product of:
            0.052020688 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.029633347 = queryNorm
            0.13576832 = fieldWeight in 866, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0546875 = fieldNorm(doc=866)
      0.071428575 = coord(1/14)
    
    Abstract
    ChatGPT, die künstliche Intelligenz der Stunde, ist von OpenAI entwickelt worden. Und OpenAI ist in der Vergangenheit nicht unerheblich von Microsoft unterstützt worden. Nun geht es ums Profitieren: Die KI soll in die Suchmaschine Bing eingebaut werden, was eine direkte Konkurrenz zu Googles Suchalgorithmen und Intelligenzen bedeutet. Bing war da bislang nicht sonderlich erfolgreich. Wie "The Information" mit Verweis auf zwei Insider berichtet, plant Microsoft, ChatGPT in seine Suchmaschine Bing einzubauen. Bereits im März könnte die neue, intelligente Suche verfügbar sein. Microsoft hatte zuvor auf der hauseigenen Messe Ignite zunächst die Integration des Bildgenerators DALL·E 2 in seine Suchmaschine angekündigt - ohne konkretes Startdatum jedoch. Fragt man ChatGPT selbst, bestätigt der Chatbot seine künftige Aufgabe noch nicht. Weiß aber um potentielle Vorteile.
  11. Sa, N.; Yuan, X.(J.): Improving the effectiveness of voice search systems through partial query modification (2022) 0.00
    4.32414E-4 = product of:
      0.0060537956 = sum of:
        0.0060537956 = weight(_text_:information in 635) [ClassicSimilarity], result of:
          0.0060537956 = score(doc=635,freq=2.0), product of:
            0.052020688 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.029633347 = queryNorm
            0.116372846 = fieldWeight in 635, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=635)
      0.071428575 = coord(1/14)
    
    Source
    Journal of the Association for Information Science and Technology. 73(2022) no.8, S.1092-1105