Search (197 results, page 1 of 10)

  • × year_i:[2020 TO 2030}
  1. Bergman, O.; Israeli, T.; Whittaker, S.: Factors hindering shared files retrieval (2020) 0.06 
    0.059406385 = product of:
  0.11881277 = sum of:
    0.11881277 = sum of:
      0.0834475 = weight(_text_:retrieval in 5843) [ClassicSimilarity], result of:
        0.0834475 = score(doc=5843,freq=20.0), product of:
          0.15791564 = queryWeight, product of:
            3.024915 = idf(docFreq=5836, maxDocs=44218)
            0.052204985 = queryNorm
          0.5284309 = fieldWeight in 5843, product of:
            4.472136 = tf(freq=20.0), with freq of:
              20.0 = termFreq=20.0
            3.024915 = idf(docFreq=5836, maxDocs=44218)
            0.0390625 = fieldNorm(doc=5843)
      0.035365272 = weight(_text_:22 in 5843) [ClassicSimilarity], result of:
        0.035365272 = score(doc=5843,freq=2.0), product of:
          0.18281296 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052204985 = queryNorm
          0.19345059 = fieldWeight in 5843, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0390625 = fieldNorm(doc=5843)
  0.5 = coord(1/2)
    Abstract
    Purpose Personal information management (PIM) is an activity in which people store information items in order to retrieve them later. The purpose of this paper is to test and quantify the effect of factors related to collection size, file properties and workload on file retrieval success and efficiency. Design/methodology/approach In the study, 289 participants retrieved 1,557 of their shared files in a naturalistic setting. The study used specially developed software designed to collect shared files' names and present them as targets for the retrieval task. The dependent variables were retrieval success, retrieval time and misstep/s. Findings Various factors compromise shared files retrieval including: collection size (large number of files), file properties (multiple versions, size of team sharing the file, time since most recent retrieval and folder depth) and workload (daily e-mails sent and received). The authors discuss theoretical reasons for these negative effects and suggest possible ways to overcome them. Originality/value Retrieval is the main reason people manage personal information. It is essential for retrieval to be successful and efficient, as information cannot be used unless it can be re-accessed. Prior PIM research has assumed that factors related to collection size, file properties and workload affect file retrieval. However, this is the first study to systematically quantify the negative effects of these factors. As each of these factors is expected to be exacerbated in the future, this study is a necessary first step toward addressing these problems.
    Date
    20. 1.2015 18:30:22
  2. Das, S.; Paik, J.H.: Gender tagging of named entities using retrieval-assisted multi-context aggregation : an unsupervised approach (2023) 0.04 
    0.04361048 = product of:
  0.08722096 = sum of:
    0.08722096 = sum of:
      0.04478263 = weight(_text_:retrieval in 941) [ClassicSimilarity], result of:
        0.04478263 = score(doc=941,freq=4.0), product of:
          0.15791564 = queryWeight, product of:
            3.024915 = idf(docFreq=5836, maxDocs=44218)
            0.052204985 = queryNorm
          0.2835858 = fieldWeight in 941, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            3.024915 = idf(docFreq=5836, maxDocs=44218)
            0.046875 = fieldNorm(doc=941)
      0.04243833 = weight(_text_:22 in 941) [ClassicSimilarity], result of:
        0.04243833 = score(doc=941,freq=2.0), product of:
          0.18281296 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052204985 = queryNorm
          0.23214069 = fieldWeight in 941, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046875 = fieldNorm(doc=941)
  0.5 = coord(1/2)
    Abstract
    Inferring the gender of named entities present in a text has several practical applications in information sciences. Existing approaches toward name gender identification rely exclusively on using the gender distributions from labeled data. In the absence of such labeled data, these methods fail. In this article, we propose a two-stage model that is able to infer the gender of names present in text without requiring explicit name-gender labels. We use coreference resolution as the backbone for our proposed model. To aid coreference resolution where the existing contextual information does not suffice, we use a retrieval-assisted context aggregation framework. We demonstrate that state-of-the-art name gender inference is possible without supervision. Our proposed method matches or outperforms several supervised approaches and commercially used methods on five English language datasets from different domains.
    Date
    22. 3.2023 12:00:14
  3. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.04 
    0.041457675 = product of:
  0.08291535 = sum of:
    0.08291535 = product of:
      0.24874605 = sum of:
        0.24874605 = weight(_text_:3a in 862) [ClassicSimilarity], result of:
          0.24874605 = score(doc=862,freq=2.0), product of:
            0.44259444 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.052204985 = queryNorm
            0.56201804 = fieldWeight in 862, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.046875 = fieldNorm(doc=862)
      0.33333334 = coord(1/3)
  0.5 = coord(1/2)
    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  4. Dietz, K.: en.wikipedia.org > 6 Mio. Artikel (2020) 0.03 
    0.034548067 = product of:
  0.06909613 = sum of:
    0.06909613 = product of:
      0.20728838 = sum of:
        0.20728838 = weight(_text_:3a in 5669) [ClassicSimilarity], result of:
          0.20728838 = score(doc=5669,freq=2.0), product of:
            0.44259444 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.052204985 = queryNorm
            0.46834838 = fieldWeight in 5669, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5669)
      0.33333334 = coord(1/3)
  0.5 = coord(1/2)
    Content
    "Die Englischsprachige Wikipedia verfügt jetzt über mehr als 6 Millionen Artikel. An zweiter Stelle kommt die deutschsprachige Wikipedia mit 2.3 Millionen Artikeln, an dritter Stelle steht die französischsprachige Wikipedia mit 2.1 Millionen Artikeln (via Researchbuzz: Firehose <https://rbfirehose.com/2020/01/24/techcrunch-wikipedia-now-has-more-than-6-million-articles-in-english/> und Techcrunch <https://techcrunch.com/2020/01/23/wikipedia-english-six-million-articles/?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+Techcrunch+%28TechCrunch%29&guccounter=1&guce_referrer=aHR0cHM6Ly9yYmZpcmVob3NlLmNvbS8yMDIwLzAxLzI0L3RlY2hjcnVuY2gtd2lraXBlZGlhLW5vdy1oYXMtbW9yZS10aGFuLTYtbWlsbGlvbi1hcnRpY2xlcy1pbi1lbmdsaXNoLw&guce_referrer_sig=AQAAAK0zHfjdDZ_spFZBF_z-zDjtL5iWvuKDumFTzm4HvQzkUfE2pLXQzGS6FGB_y-VISdMEsUSvkNsg2U_NWQ4lwWSvOo3jvXo1I3GtgHpP8exukVxYAnn5mJspqX50VHIWFADHhs5AerkRn3hMRtf_R3F1qmEbo8EROZXp328HMC-o>). 250120 via digithek ch = #fineBlog s.a.: Angesichts der Veröffentlichung des 6-millionsten Artikels vergangene Woche in der englischsprachigen Wikipedia hat die Community-Zeitungsseite "Wikipedia Signpost" ein Moratorium bei der Veröffentlichung von Unternehmensartikeln gefordert. Das sei kein Vorwurf gegen die Wikimedia Foundation, aber die derzeitigen Maßnahmen, um die Enzyklopädie gegen missbräuchliches undeklariertes Paid Editing zu schützen, funktionierten ganz klar nicht. *"Da die ehrenamtlichen Autoren derzeit von Werbung in Gestalt von Wikipedia-Artikeln überwältigt werden, und da die WMF nicht in der Lage zu sein scheint, dem irgendetwas entgegenzusetzen, wäre der einzige gangbare Weg für die Autoren, fürs erste die Neuanlage von Artikeln über Unternehmen zu untersagen"*, schreibt der Benutzer Smallbones in seinem Editorial <https://en.wikipedia.org/wiki/Wikipedia:Wikipedia_Signpost/2020-01-27/From_the_editor> zur heutigen Ausgabe."
  5. Gabler, S.: Vergabe von DDC-Sachgruppen mittels eines Schlagwort-Thesaurus (2021) 0.03 
    0.034548067 = product of:
  0.06909613 = sum of:
    0.06909613 = product of:
      0.20728838 = sum of:
        0.20728838 = weight(_text_:3a in 1000) [ClassicSimilarity], result of:
          0.20728838 = score(doc=1000,freq=2.0), product of:
            0.44259444 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.052204985 = queryNorm
            0.46834838 = fieldWeight in 1000, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1000)
      0.33333334 = coord(1/3)
  0.5 = coord(1/2)
    Content
    Master thesis Master of Science (Library and Information Studies) (MSc), Universität Wien. Advisor: Christoph Steiner. Vgl.: https://www.researchgate.net/publication/371680244_Vergabe_von_DDC-Sachgruppen_mittels_eines_Schlagwort-Thesaurus. DOI: 10.25365/thesis.70030. Vgl. dazu die Präsentation unter: https://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=web&cd=&ved=0CAIQw7AJahcKEwjwoZzzytz_AhUAAAAAHQAAAAAQAg&url=https%3A%2F%2Fwiki.dnb.de%2Fdownload%2Fattachments%2F252121510%2FDA3%2520Workshop-Gabler.pdf%3Fversion%3D1%26modificationDate%3D1671093170000%26api%3Dv2&psig=AOvVaw0szwENK1or3HevgvIDOfjx&ust=1687719410889597&opi=89978449.
  6. Hartel, J.: ¬The red thread of information (2020) 0.03 
    0.030876845 = product of:
  0.06175369 = sum of:
    0.06175369 = sum of:
      0.026388418 = weight(_text_:retrieval in 5839) [ClassicSimilarity], result of:
        0.026388418 = score(doc=5839,freq=2.0), product of:
          0.15791564 = queryWeight, product of:
            3.024915 = idf(docFreq=5836, maxDocs=44218)
            0.052204985 = queryNorm
          0.16710453 = fieldWeight in 5839, 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=5839)
      0.035365272 = weight(_text_:22 in 5839) [ClassicSimilarity], result of:
        0.035365272 = score(doc=5839,freq=2.0), product of:
          0.18281296 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052204985 = queryNorm
          0.19345059 = fieldWeight in 5839, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0390625 = fieldNorm(doc=5839)
  0.5 = coord(1/2)
    Abstract
    Purpose In The Invisible Substrate of Information Science, a landmark article about the discipline of information science, Marcia J. Bates wrote that ".we are always looking for the red thread of information in the social texture of people's lives" (1999a, p. 1048). To sharpen our understanding of information science and to elaborate Bates' idea, the work at hand answers the question: Just what does the red thread of information entail? Design/methodology/approach Through a close reading of Bates' oeuvre and by applying concepts from the reference literature of information science, nine composite entities that qualify as the red thread of information are identified, elaborated, and related to existing concepts in the information science literature. In the spirit of a scientist-poet (White, 1999), several playful metaphors related to the color red are employed. Findings Bates' red thread of information entails: terms, genres, literatures, classification systems, scholarly communication, information retrieval, information experience, information institutions, and information policy. This same constellation of phenomena can be found in resonant visions of information science, namely, domain analysis (Hjørland, 2002), ethnography of infrastructure (Star, 1999), and social epistemology (Shera, 1968). Research limitations/implications With the vital vermilion filament in clear view, newcomers can more easily engage the material, conceptual, and social machinery of information science, and specialists are reminded of what constitutes information science as a whole. Future researchers and scientist-poets may wish to supplement the nine composite entities with additional, emergent information phenomena. Originality/value Though the explication of information science that follows is relatively orthodox and time-bound, the paper offers an imaginative, accessible, yet technically precise way of understanding the field.
    Date
    30. 4.2020 21:03:22
  7. Chi, Y.; He, D.; Jeng, W.: Laypeople's source selection in online health information-seeking process (2020) 0.03 
    0.030876845 = product of:
  0.06175369 = sum of:
    0.06175369 = sum of:
      0.026388418 = weight(_text_:retrieval in 34) [ClassicSimilarity], result of:
        0.026388418 = score(doc=34,freq=2.0), product of:
          0.15791564 = queryWeight, product of:
            3.024915 = idf(docFreq=5836, maxDocs=44218)
            0.052204985 = queryNorm
          0.16710453 = fieldWeight in 34, 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=34)
      0.035365272 = weight(_text_:22 in 34) [ClassicSimilarity], result of:
        0.035365272 = score(doc=34,freq=2.0), product of:
          0.18281296 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052204985 = queryNorm
          0.19345059 = fieldWeight in 34, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0390625 = fieldNorm(doc=34)
  0.5 = coord(1/2)
    Abstract
    For laypeople, searching online health information resources can be challenging due to topic complexity and the large number of online sources with differing quality. The goal of this article is to examine, among all the available online sources, which online sources laypeople select to address their health-related information needs, and whether or how much the severity of a health condition influences their selection. Twenty-four participants were recruited individually, and each was asked (using a retrieval system called HIS) to search for information regarding a severe health condition and a mild health condition, respectively. The selected online health information sources were automatically captured by the HIS system and classified at both the website and webpage levels. Participants' selection behavior patterns were then plotted across the whole information-seeking process. Our results demonstrate that laypeople's source selection fluctuates during the health information-seeking process, and also varies by the severity of health conditions. This study reveals laypeople's real usage of different types of online health information sources, and engenders implications to the design of search engines, as well as the development of health literacy programs.
    Date
    12.11.2020 13:22:09
  8. Wiesenmüller, H.: Verbale Erschließung in Katalogen und Discovery-Systemen : Überlegungen zur Qualität (2021) 0.03 
    0.030876845 = product of:
  0.06175369 = sum of:
    0.06175369 = sum of:
      0.026388418 = weight(_text_:retrieval in 374) [ClassicSimilarity], result of:
        0.026388418 = score(doc=374,freq=2.0), product of:
          0.15791564 = queryWeight, product of:
            3.024915 = idf(docFreq=5836, maxDocs=44218)
            0.052204985 = queryNorm
          0.16710453 = fieldWeight in 374, 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=374)
      0.035365272 = weight(_text_:22 in 374) [ClassicSimilarity], result of:
        0.035365272 = score(doc=374,freq=2.0), product of:
          0.18281296 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052204985 = queryNorm
          0.19345059 = fieldWeight in 374, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0390625 = fieldNorm(doc=374)
  0.5 = coord(1/2)
    Date
    24. 9.2021 12:22:02
    Theme
    Verbale Doksprachen im Online-Retrieval
  9. Jiang, Y.; Meng, R.; Huang, Y.; Lu, W.; Liu, J.: Generating keyphrases for readers : a controllable keyphrase generation framework (2023) 0.03 
    0.030876845 = product of:
  0.06175369 = sum of:
    0.06175369 = sum of:
      0.026388418 = weight(_text_:retrieval in 1012) [ClassicSimilarity], result of:
        0.026388418 = score(doc=1012,freq=2.0), product of:
          0.15791564 = queryWeight, product of:
            3.024915 = idf(docFreq=5836, maxDocs=44218)
            0.052204985 = queryNorm
          0.16710453 = fieldWeight in 1012, 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=1012)
      0.035365272 = weight(_text_:22 in 1012) [ClassicSimilarity], result of:
        0.035365272 = score(doc=1012,freq=2.0), product of:
          0.18281296 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052204985 = queryNorm
          0.19345059 = fieldWeight in 1012, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0390625 = fieldNorm(doc=1012)
  0.5 = coord(1/2)
    Abstract
    With the wide application of keyphrases in many Information Retrieval (IR) and Natural Language Processing (NLP) tasks, automatic keyphrase prediction has been emerging. However, these statistically important phrases are contributing increasingly less to the related tasks because the end-to-end learning mechanism enables models to learn the important semantic information of the text directly. Similarly, keyphrases are of little help for readers to quickly grasp the paper's main idea because the relationship between the keyphrase and the paper is not explicit to readers. Therefore, we propose to generate keyphrases with specific functions for readers to bridge the semantic gap between them and the information producers, and verify the effectiveness of the keyphrase function for assisting users' comprehension with a user experiment. A controllable keyphrase generation framework (the CKPG) that uses the keyphrase function as a control code to generate categorized keyphrases is proposed and implemented based on Transformer, BART, and T5, respectively. For the Computer Science domain, the Macro-avgs of , , and on the Paper with Code dataset are up to 0.680, 0.535, and 0.558, respectively. Our experimental results indicate the effectiveness of the CKPG models.
    Date
    22. 6.2023 14:55:20
  10. Bedford, D.: Knowledge architectures : structures and semantics (2021) 0.03 
    0.029073652 = product of:
  0.058147304 = sum of:
    0.058147304 = sum of:
      0.029855086 = weight(_text_:retrieval in 566) [ClassicSimilarity], result of:
        0.029855086 = score(doc=566,freq=4.0), product of:
          0.15791564 = queryWeight, product of:
            3.024915 = idf(docFreq=5836, maxDocs=44218)
            0.052204985 = queryNorm
          0.18905719 = fieldWeight in 566, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            3.024915 = idf(docFreq=5836, maxDocs=44218)
            0.03125 = fieldNorm(doc=566)
      0.028292218 = weight(_text_:22 in 566) [ClassicSimilarity], result of:
        0.028292218 = score(doc=566,freq=2.0), product of:
          0.18281296 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052204985 = queryNorm
          0.15476047 = fieldWeight in 566, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.03125 = fieldNorm(doc=566)
  0.5 = coord(1/2)
    Content
    Section 1 Context and purpose of knowledge architecture -- 1 Making the case for knowledge architecture -- 2 The landscape of knowledge assets -- 3 Knowledge architecture and design -- 4 Knowledge architecture reference model -- 5 Knowledge architecture segments -- Section 2 Designing for availability -- 6 Knowledge object modeling -- 7 Knowledge structures for encoding, formatting, and packaging -- 8 Functional architecture for identification and distinction -- 9 Functional architectures for knowledge asset disposition and destruction -- 10 Functional architecture designs for knowledge preservation and conservation -- Section 3 Designing for accessibility -- 11 Functional architectures for knowledge seeking and discovery -- 12 Functional architecture for knowledge search -- 13 Functional architecture for knowledge categorization -- 14 Functional architectures for indexing and keywording -- 15 Functional architecture for knowledge semantics -- 16 Functional architecture for knowledge abstraction and surrogation -- Section 4 Functional architectures to support knowledge consumption -- 17 Functional architecture for knowledge augmentation, derivation, and synthesis -- 18 Functional architecture to manage risk and harm -- 19 Functional architectures for knowledge authentication and provenance -- 20 Functional architectures for securing knowledge assets -- 21 Functional architectures for authorization and asset management -- Section 5 Pulling it all together - the big picture knowledge architecture -- 22 Functional architecture for knowledge metadata and metainformation -- 23 The whole knowledge architecture - pulling it all together
    LCSH
    Information storage and retrieval systems / Management
    Subject
    Information storage and retrieval systems / Management
  11. Eibl, M.; Haupt, J.; Kahl, S.; Taubert, S.; Wilhelm-Stein, T.: Audio- und Musik-Retrieval (2023) 0.03 
    0.0261232 = product of:
  0.0522464 = sum of:
    0.0522464 = product of:
      0.1044928 = sum of:
        0.1044928 = weight(_text_:retrieval in 802) [ClassicSimilarity], result of:
          0.1044928 = score(doc=802,freq=16.0), product of:
            0.15791564 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.052204985 = queryNorm
            0.6617001 = fieldWeight in 802, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0546875 = fieldNorm(doc=802)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Das Gebiet Audio-Retrieval kann grob in drei Bereiche unterteilt werden: Musik-Retrieval, Retrieval gesprochener Sprache und Retrieval akustischer Ereignisse. Alle drei Bereiche gehen i. d. R. vom Audiosignal als Quelle aus, welches über eine Signalanalyse, meist eine Spektralanalyse über eine Fouriertransformation, weiterverarbeitet und in eine für das Retrieval geeignete Beschreibung gebracht wird. Dabei gibt es auch alternative Ansätze, wie z. B. die Nutzung der hier nicht diskutierten MIDI-Codierung im Musik-Retrieval, die ohne ein akustisches Signal auskommt und bereits eine für das Retrieval geeignete Form der Kodierung als Grundlage hat.
  12. ¬Der Student aus dem Computer (2023) 0.02 
    0.02475569 = product of:
  0.04951138 = sum of:
    0.04951138 = product of:
      0.09902276 = sum of:
        0.09902276 = weight(_text_:22 in 1079) [ClassicSimilarity], result of:
          0.09902276 = score(doc=1079,freq=2.0), product of:
            0.18281296 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052204985 = queryNorm
            0.5416616 = fieldWeight in 1079, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.109375 = fieldNorm(doc=1079)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Date
    27. 1.2023 16:22:55
  13. Strecker, D.: Dataset Retrieval : Informationsverhalten von Datensuchenden und das Ökosystem von Data-Retrieval-Systemen (2022) 0.02 
    0.02360252 = product of:
  0.04720504 = sum of:
    0.04720504 = product of:
      0.09441008 = sum of:
        0.09441008 = weight(_text_:retrieval in 4021) [ClassicSimilarity], result of:
          0.09441008 = score(doc=4021,freq=10.0), product of:
            0.15791564 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.052204985 = queryNorm
            0.59785134 = fieldWeight in 4021, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0625 = fieldNorm(doc=4021)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Verschiedene Stakeholder fordern eine bessere Verfügbarkeit von Forschungsdaten. Der Erfolg dieser Initiativen hängt wesentlich von einer guten Auffindbarkeit der publizierten Datensätze ab, weshalb Dataset Retrieval an Bedeutung gewinnt. Dataset Retrieval ist eine Sonderform von Information Retrieval, die sich mit dem Auffinden von Datensätzen befasst. Dieser Beitrag fasst aktuelle Forschungsergebnisse über das Informationsverhalten von Datensuchenden zusammen. Anschließend werden beispielhaft zwei Suchdienste verschiedener Ausrichtung vorgestellt und verglichen. Um darzulegen, wie diese Dienste ineinandergreifen, werden inhaltliche Überschneidungen von Datenbeständen genutzt, um den Metadatenaustausch zu analysieren.
  14. Jaeger, L.: Wissenschaftler versus Wissenschaft (2020) 0.02 
    0.021219164 = product of:
  0.04243833 = sum of:
    0.04243833 = product of:
      0.08487666 = sum of:
        0.08487666 = weight(_text_:22 in 4156) [ClassicSimilarity], result of:
          0.08487666 = score(doc=4156,freq=2.0), product of:
            0.18281296 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052204985 = queryNorm
            0.46428138 = fieldWeight in 4156, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.09375 = fieldNorm(doc=4156)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Date
    2. 3.2020 14:08:22
  15. Ibrahim, G.M.; Taylor, M.: Krebszellen manipulieren Neurone : Gliome (2023) 0.02 
    0.021219164 = product of:
  0.04243833 = sum of:
    0.04243833 = product of:
      0.08487666 = sum of:
        0.08487666 = weight(_text_:22 in 1203) [ClassicSimilarity], result of:
          0.08487666 = score(doc=1203,freq=2.0), product of:
            0.18281296 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052204985 = queryNorm
            0.46428138 = fieldWeight in 1203, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.09375 = fieldNorm(doc=1203)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Source
    Spektrum der Wissenschaft. 2023, H.10, S.22-24
  16. Wu, Z.; Lu, C.; Zhao, Y.; Xie, J.; Zou, D.; Su, X.: ¬The protection of user preference privacy in personalized information retrieval : challenges and overviews (2021) 0.02 
    0.018659428 = product of:
  0.037318856 = sum of:
    0.037318856 = product of:
      0.07463771 = sum of:
        0.07463771 = weight(_text_:retrieval in 520) [ClassicSimilarity], result of:
          0.07463771 = score(doc=520,freq=16.0), product of:
            0.15791564 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.052204985 = queryNorm
            0.47264296 = fieldWeight in 520, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=520)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    This paper reviews a large number of research achievements relevant to user privacy protection in an untrusted network environment, and then analyzes and evaluates their application limitations in personalized information retrieval, to establish the conditional constraints that an effective approach for user preference privacy protection in personalized information retrieval should meet, thus providing a basic reference for the solution of this problem. First, based on the basic framework of a personalized information retrieval platform, we establish a complete set of constraints for user preference privacy protection in terms of security, usability, efficiency, and accuracy. Then, we comprehensively review the technical features for all kinds of popular methods for user privacy protection, and analyze their application limitations in personalized information retrieval, according to the constraints of preference privacy protection. The results show that personalized information retrieval has higher requirements for users' privacy protection, i.e., it is required to comprehensively improve the security of users' preference privacy on the untrusted server-side, under the precondition of not changing the platform, algorithm, efficiency, and accuracy of personalized information retrieval. However, all kinds of existing privacy methods still cannot meet the above requirements. This paper is an important study attempt to the problem of user preference privacy protection of personalized information retrieval, which can provide a basic reference and direction for the further study of the problem.
  17. Dang, E.K.F.; Luk, R.W.P.; Allan, J.: ¬A retrieval model family based on the probability ranking principle for ad hoc retrieval (2022) 0.02 
    0.018471893 = product of:
  0.036943786 = sum of:
    0.036943786 = product of:
      0.07388757 = sum of:
        0.07388757 = weight(_text_:retrieval in 638) [ClassicSimilarity], result of:
          0.07388757 = score(doc=638,freq=8.0), product of:
            0.15791564 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.052204985 = queryNorm
            0.46789268 = fieldWeight in 638, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0546875 = fieldNorm(doc=638)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Many successful retrieval models are derived based on or conform to the probability ranking principle (PRP). We present a new derivation of a document ranking function given by the probability of relevance of a document, conforming to the PRP. Our formulation yields a family of retrieval models, called probabilistic binary relevance (PBR) models, with various instantiations obtained by different probability estimations. By extensive experiments on a range of TREC collections, improvement of the PBR models over some established baselines with statistical significance is observed, especially in the large Clueweb09 Cat-B collection.
  18. Aizawa, A.; Kohlhase, M.: Mathematical information retrieval (2021) 0.02 
    0.018471893 = product of:
  0.036943786 = sum of:
    0.036943786 = product of:
      0.07388757 = sum of:
        0.07388757 = weight(_text_:retrieval in 667) [ClassicSimilarity], result of:
          0.07388757 = score(doc=667,freq=8.0), product of:
            0.15791564 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.052204985 = queryNorm
            0.46789268 = fieldWeight in 667, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0546875 = fieldNorm(doc=667)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    We present an overview of the NTCIR Math Tasks organized during NTCIR-10, 11, and 12. These tasks are primarily dedicated to techniques for searching mathematical content with formula expressions. In this chapter, we first summarize the task design and introduce test collections generated in the tasks. We also describe the features and main challenges of mathematical information retrieval systems and discuss future perspectives in the field.
    Series
    ¬The Information retrieval series, vol 43
    Source
    Evaluating information retrieval and access tasks. Eds.: Sakai, T., Oard, D., Kando, N. [https://doi.org/10.1007/978-981-15-5554-1_12]
  19. Mandl, T.; Diem, S.: Bild- und Video-Retrieval (2023) 0.02 
    0.01770189 = product of:
  0.03540378 = sum of:
    0.03540378 = product of:
      0.07080756 = sum of:
        0.07080756 = weight(_text_:retrieval in 801) [ClassicSimilarity], result of:
          0.07080756 = score(doc=801,freq=10.0), product of:
            0.15791564 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.052204985 = queryNorm
            0.44838852 = fieldWeight in 801, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.046875 = fieldNorm(doc=801)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Digitale Bildverarbeitung hat längst den Alltag erreicht: Automatisierte Passkontrollen, Gesichtserkennung auf dem Mobiltelefon und Apps zum Bestimmen von Pflanzen anhand von Fotos sind nur einige Beispiele für den Einsatz dieser Technologie. Digitale Bildverarbeitung zur Analyse der Inhalte von Bildern kann den Zugang zu Wissen verbessern und ist somit relevant für die Informationswissenschaft. Häufig greifen Systeme bei der Suche nach visueller Information nach wie vor auf beschreibende Metadaten zu, weil diese sprachbasierten Methoden für Massendaten meist robust funktionieren. Der Fokus liegt in diesem Beitrag auf automatischer Inhaltsanalyse von Bildern (content based image retrieval) und nicht auf reinen Metadaten-Systemen, welche Wörter für die Beschreibung von Bildern nutzen (s. Kapitel B 9 Metadaten) und somit letztlich Text-Retrieval ausführen (concept based image retrieval) (s. Kapitel C 1 Informationswissenschaftliche Perspektiven des Information Retrieval).
  20. Petras, V.; Womser-Hacker, C.: Evaluation im Information Retrieval (2023) 0.02 
    0.01770189 = product of:
  0.03540378 = sum of:
    0.03540378 = product of:
      0.07080756 = sum of:
        0.07080756 = weight(_text_:retrieval in 808) [ClassicSimilarity], result of:
          0.07080756 = score(doc=808,freq=10.0), product of:
            0.15791564 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.052204985 = queryNorm
            0.44838852 = fieldWeight in 808, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.046875 = fieldNorm(doc=808)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Das Ziel einer Evaluation ist die Überprüfung, ob bzw. in welchem Ausmaß ein Informationssystem die an das System gestellten Anforderungen erfüllt. Informationssysteme können aus verschiedenen Perspektiven evaluiert werden. Für eine ganzheitliche Evaluation (als Synonym wird auch Evaluierung benutzt), die unterschiedliche Qualitätsaspekte betrachtet (z. B. wie gut ein System relevante Dokumente rankt, wie schnell ein System die Suche durchführt, wie die Ergebnispräsentation gestaltet ist oder wie Suchende durch das System geführt werden) und die Erfüllung mehrerer Anforderungen überprüft, empfiehlt es sich, sowohl eine perspektivische als auch methodische Triangulation (d. h. der Einsatz von mehreren Ansätzen zur Qualitätsüberprüfung) vorzunehmen. Im Information Retrieval (IR) konzentriert sich die Evaluation auf die Qualitätseinschätzung der Suchfunktion eines Information-Retrieval-Systems (IRS), wobei oft zwischen systemzentrierter und nutzerzentrierter Evaluation unterschieden wird. Dieses Kapitel setzt den Fokus auf die systemzentrierte Evaluation, während andere Kapitel dieses Handbuchs andere Evaluationsansätze diskutieren (s. Kapitel C 4 Interaktives Information Retrieval, C 7 Cross-Language Information Retrieval und D 1 Information Behavior).

Authors

Languages

  • e 148
  • d 47
  • pt 2
  • More… Less…

Types

  • a 183
  • el 29
  • p 5
  • m 4
  • s 2
  • A 1
  • EL 1
  • x 1
  • More… Less…

Themes

Subjects

Classifications