Search (2782 results, page 1 of 140)

  • × year_i:[2000 TO 2010}
  1. Justice, A.: 12th American Society for Information science & Technology, Special Interest Group Classification Research : Classification Research workshop (2002) 0.12 
    0.12441054 = product of:
  0.18661581 = sum of:
    0.13763799 = weight(_text_:interest in 1122) [ClassicSimilarity], result of:
      0.13763799 = score(doc=1122,freq=2.0), product of:
        0.25074318 = queryWeight, product of:
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.05046903 = queryNorm
        0.54892015 = fieldWeight in 1122, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.078125 = fieldNorm(doc=1122)
    0.048977822 = product of:
      0.097955644 = sum of:
        0.097955644 = weight(_text_:classification in 1122) [ClassicSimilarity], result of:
          0.097955644 = score(doc=1122,freq=6.0), product of:
            0.16072905 = queryWeight, product of:
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.05046903 = queryNorm
            0.6094458 = fieldWeight in 1122, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.078125 = fieldNorm(doc=1122)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Footnote
    The workshop papers will be published in final versions in mid-2002 by Information Today as Advances in Classification Research; vol 12
  2. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.12 
    0.11997249 = product of:
  0.17995873 = sum of:
    0.080158204 = product of:
      0.2404746 = sum of:
        0.2404746 = weight(_text_:3a in 562) [ClassicSimilarity], result of:
          0.2404746 = score(doc=562,freq=2.0), product of:
            0.427877 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.05046903 = queryNorm
            0.56201804 = fieldWeight in 562, 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=562)
      0.33333334 = coord(1/3)
    0.09980053 = sum of:
      0.058773387 = weight(_text_:classification in 562) [ClassicSimilarity], result of:
        0.058773387 = score(doc=562,freq=6.0), product of:
          0.16072905 = queryWeight, product of:
            3.1847067 = idf(docFreq=4974, maxDocs=44218)
            0.05046903 = queryNorm
          0.3656675 = fieldWeight in 562, product of:
            2.4494898 = tf(freq=6.0), with freq of:
              6.0 = termFreq=6.0
            3.1847067 = idf(docFreq=4974, maxDocs=44218)
            0.046875 = fieldNorm(doc=562)
      0.04102714 = weight(_text_:22 in 562) [ClassicSimilarity], result of:
        0.04102714 = score(doc=562,freq=2.0), product of:
          0.17673394 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05046903 = queryNorm
          0.23214069 = fieldWeight in 562, 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=562)
  0.6666667 = coord(2/3)
    Abstract
    Document representations for text classification are typically based on the classical Bag-Of-Words paradigm. This approach comes with deficiencies that motivate the integration of features on a higher semantic level than single words. In this paper we propose an enhancement of the classical document representation through concepts extracted from background knowledge. Boosting is used for actual classification. Experimental evaluations on two well known text corpora support our approach through consistent improvement of the results.
    Content
    Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.
    Date
    8. 1.2013 10:22:32
  3. Beghtol, C.: Naïve classification systems and the global information society (2004) 0.12 
    0.118548766 = product of:
  0.17782314 = sum of:
    0.068818994 = weight(_text_:interest in 3483) [ClassicSimilarity], result of:
      0.068818994 = score(doc=3483,freq=2.0), product of:
        0.25074318 = queryWeight, product of:
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.05046903 = queryNorm
        0.27446008 = fieldWeight in 3483, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3483)
    0.10900415 = sum of:
      0.07481486 = weight(_text_:classification in 3483) [ClassicSimilarity], result of:
        0.07481486 = score(doc=3483,freq=14.0), product of:
          0.16072905 = queryWeight, product of:
            3.1847067 = idf(docFreq=4974, maxDocs=44218)
            0.05046903 = queryNorm
          0.46547192 = fieldWeight in 3483, product of:
            3.7416575 = tf(freq=14.0), with freq of:
              14.0 = termFreq=14.0
            3.1847067 = idf(docFreq=4974, maxDocs=44218)
            0.0390625 = fieldNorm(doc=3483)
      0.034189284 = weight(_text_:22 in 3483) [ClassicSimilarity], result of:
        0.034189284 = score(doc=3483,freq=2.0), product of:
          0.17673394 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05046903 = queryNorm
          0.19345059 = fieldWeight in 3483, 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=3483)
  0.6666667 = coord(2/3)
    Abstract
    Classification is an activity that transcends time and space and that bridges the divisions between different languages and cultures, including the divisions between academic disciplines. Classificatory activity, however, serves different purposes in different situations. Classifications for infonnation retrieval can be called "professional" classifications and classifications in other fields can be called "naïve" classifications because they are developed by people who have no particular interest in classificatory issues. The general purpose of naïve classification systems is to discover new knowledge. In contrast, the general purpose of information retrieval classifications is to classify pre-existing knowledge. Different classificatory purposes may thus inform systems that are intended to span the cultural specifics of the globalized information society. This paper builds an previous research into the purposes and characteristics of naïve classifications. It describes some of the relationships between the purpose and context of a naive classification, the units of analysis used in it, and the theory that the context and the units of analysis imply.
    Footnote
    Vgl.: Jacob, E.K.: Proposal for a classification of classifications built on Beghtol's distinction between "Naïve Classification" and "Professional Classification". In: Knowledge organization. 37(2010) no.2, S.111-120.
    Pages
    S.19-22
  4. Camacho-Miñano, M.-del-Mar; Núñez-Nickel, M.: ¬The multilayered nature of reference selection (2009) 0.11 
    0.10502851 = product of:
  0.15754277 = sum of:
    0.082582794 = weight(_text_:interest in 2751) [ClassicSimilarity], result of:
      0.082582794 = score(doc=2751,freq=2.0), product of:
        0.25074318 = queryWeight, product of:
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.05046903 = queryNorm
        0.3293521 = fieldWeight in 2751, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.046875 = fieldNorm(doc=2751)
    0.07495997 = sum of:
      0.03393283 = weight(_text_:classification in 2751) [ClassicSimilarity], result of:
        0.03393283 = score(doc=2751,freq=2.0), product of:
          0.16072905 = queryWeight, product of:
            3.1847067 = idf(docFreq=4974, maxDocs=44218)
            0.05046903 = queryNorm
          0.21111822 = fieldWeight in 2751, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.1847067 = idf(docFreq=4974, maxDocs=44218)
            0.046875 = fieldNorm(doc=2751)
      0.04102714 = weight(_text_:22 in 2751) [ClassicSimilarity], result of:
        0.04102714 = score(doc=2751,freq=2.0), product of:
          0.17673394 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05046903 = queryNorm
          0.23214069 = fieldWeight in 2751, 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=2751)
  0.6666667 = coord(2/3)
    Abstract
    Why authors choose some references in preference to others is a question that is still not wholly answered despite its being of interest to scientists. The relevance of references is twofold: They are a mechanism for tracing the evolution of science, and because they enhance the image of the cited authors, citations are a widely known and used indicator of scientific endeavor. Following an extensive review of the literature, we selected all papers that seek to answer the central question and demonstrate that the existing theories are not sufficient: Neither citation nor indicator theory provides a complete and convincing answer. Some perspectives in this arena remain, which are isolated from the core literature. The purpose of this article is to offer a fresh perspective on a 30-year-old problem by extending the context of the discussion. We suggest reviving the discussion about citation theories with a new perspective, that of the readers, by layers or phases, in the final choice of references, allowing for a new classification in which any paper, to date, could be included.
    Date
    22. 3.2009 19:05:07
  5. Rafferty, P.: ¬The representation of knowledge in library classification schemes (2001) 0.10 
    0.095837384 = product of:
  0.14375608 = sum of:
    0.082582794 = weight(_text_:interest in 640) [ClassicSimilarity], result of:
      0.082582794 = score(doc=640,freq=2.0), product of:
        0.25074318 = queryWeight, product of:
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.05046903 = queryNorm
        0.3293521 = fieldWeight in 640, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.046875 = fieldNorm(doc=640)
    0.061173283 = product of:
      0.122346565 = sum of:
        0.122346565 = weight(_text_:classification in 640) [ClassicSimilarity], result of:
          0.122346565 = score(doc=640,freq=26.0), product of:
            0.16072905 = queryWeight, product of:
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.05046903 = queryNorm
            0.76119757 = fieldWeight in 640, product of:
              5.0990195 = tf(freq=26.0), with freq of:
                26.0 = termFreq=26.0
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.046875 = fieldNorm(doc=640)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    This article explores the representation of knowledge through the discursive practice of 'general' or 'universal' classification schemes. These classification schemes were constructed within a philosophical framework which viewed `man' as the central focus in the universe, which believed in progress through science and research, and which privileged written documentation over other forms. All major classification schemes are built on clearly identifiable systems of knowledge, and all classification schemes, as discursive formations, regulate the ways in which knowledge is made accessible. Of particular interest in determining how knowledge is represented in classification schemes are the following: - Main classes: classification theorists have attempted to 'discipline epistemology' in the sense of imposing main class structures with the view to simplifying access to knowledge in documents for library users. - Notational language: a number of classification theorists were particularly interested in the establishment of symbolic languages through notation. The article considers these aspects of classification theory in relation to: the Dewey Decimal Classification scheme; Otlet and La Fontaine's Universal Bibliographic Classification and the International Institute of Bibliography; Henry Evelyn Bliss's Bibliographic Classification; and S.R. Ranganathan's Colon Classification.
  6. Mengle, S.; Goharian, N.: Passage detection using text classification (2009) 0.10 
    0.09533234 = product of:
  0.1429985 = sum of:
    0.068818994 = weight(_text_:interest in 2765) [ClassicSimilarity], result of:
      0.068818994 = score(doc=2765,freq=2.0), product of:
        0.25074318 = queryWeight, product of:
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.05046903 = queryNorm
        0.27446008 = fieldWeight in 2765, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2765)
    0.07417951 = sum of:
      0.039990224 = weight(_text_:classification in 2765) [ClassicSimilarity], result of:
        0.039990224 = score(doc=2765,freq=4.0), product of:
          0.16072905 = queryWeight, product of:
            3.1847067 = idf(docFreq=4974, maxDocs=44218)
            0.05046903 = queryNorm
          0.24880521 = fieldWeight in 2765, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            3.1847067 = idf(docFreq=4974, maxDocs=44218)
            0.0390625 = fieldNorm(doc=2765)
      0.034189284 = weight(_text_:22 in 2765) [ClassicSimilarity], result of:
        0.034189284 = score(doc=2765,freq=2.0), product of:
          0.17673394 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05046903 = queryNorm
          0.19345059 = fieldWeight in 2765, 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=2765)
  0.6666667 = coord(2/3)
    Abstract
    Passages can be hidden within a text to circumvent their disallowed transfer. Such release of compartmentalized information is of concern to all corporate and governmental organizations. Passage retrieval is well studied; we posit, however, that passage detection is not. Passage retrieval is the determination of the degree of relevance of blocks of text, namely passages, comprising a document. Rather than determining the relevance of a document in its entirety, passage retrieval determines the relevance of the individual passages. As such, modified traditional information-retrieval techniques compare terms found in user queries with the individual passages to determine a similarity score for passages of interest. In passage detection, passages are classified into predetermined categories. More often than not, passage detection techniques are deployed to detect hidden paragraphs in documents. That is, to hide information, documents are injected with hidden text into passages. Rather than matching query terms against passages to determine their relevance, using text-mining techniques, the passages are classified. Those documents with hidden passages are defined as infected. Thus, simply stated, passage retrieval is the search for passages relevant to a user query, while passage detection is the classification of passages. That is, in passage detection, passages are labeled with one or more categories from a set of predetermined categories. We present a keyword-based dynamic passage approach (KDP) and demonstrate that KDP outperforms statistically significantly (99% confidence) the other document-splitting approaches by 12% to 18% in the passage detection and passage category-prediction tasks. Furthermore, we evaluate the effects of the feature selection, passage length, ambiguous passages, and finally training-data category distribution on passage-detection accuracy.
    Date
    22. 3.2009 19:14:43
  7. Synak, M.; Dabrowski, M.; Kruk, S.R.: Semantic Web and ontologies (2009) 0.09 
    0.09164122 = product of:
  0.13746183 = sum of:
    0.110110395 = weight(_text_:interest in 3376) [ClassicSimilarity], result of:
      0.110110395 = score(doc=3376,freq=2.0), product of:
        0.25074318 = queryWeight, product of:
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.05046903 = queryNorm
        0.43913615 = fieldWeight in 3376, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.0625 = fieldNorm(doc=3376)
    0.027351426 = product of:
      0.054702852 = sum of:
        0.054702852 = weight(_text_:22 in 3376) [ClassicSimilarity], result of:
          0.054702852 = score(doc=3376,freq=2.0), product of:
            0.17673394 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05046903 = queryNorm
            0.30952093 = fieldWeight in 3376, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=3376)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    This chapter presents ontologies and their role in the creation of the Semantic Web. Ontologies hold special interest, because they are very closely related to the way we understand the world. They provide common understanding, the very first step to successful communication. In following sections, we will present ontologies, how they are created and used. We will describe available tools for specifying and working with ontologies.
    Date
    31. 7.2010 16:58:22
  8. Beghtol, C.: Classification for information retrieval and classification for knowledge discovery : relationships between "professional" and "naïve" classifications (2003) 0.09 
    0.09154332 = product of:
  0.13731498 = sum of:
    0.09732476 = weight(_text_:interest in 3021) [ClassicSimilarity], result of:
      0.09732476 = score(doc=3021,freq=4.0), product of:
        0.25074318 = queryWeight, product of:
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.05046903 = queryNorm
        0.38814518 = fieldWeight in 3021, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3021)
    0.039990224 = product of:
      0.07998045 = sum of:
        0.07998045 = weight(_text_:classification in 3021) [ClassicSimilarity], result of:
          0.07998045 = score(doc=3021,freq=16.0), product of:
            0.16072905 = queryWeight, product of:
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.05046903 = queryNorm
            0.49761042 = fieldWeight in 3021, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3021)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    Classification is a transdisciplinary activity that occurs during all human pursuits. Classificatory activity, however, serves different purposes in different situations. In information retrieval, the primary purpose of classification is to find knowledge that already exists, but one of the purposes of classification in other fields is to discover new knowledge. In this paper, classifications for information retrieval are called "professional" classifications because they are devised by people who have a professional interest in classification, and classifications for knowledge discovery are called "naive" classifications because they are devised by people who have no particular interest in studying classification as an end in itself. This paper compares the overall purposes and methods of these two kinds of classifications and provides a general model of the relationships between the two kinds of classificatory activity in the context of information studies. This model addresses issues of the influence of scholarly activity and communication an the creation and revision of classifications for the purposes of information retrieval and for the purposes of knowledge discovery. Further comparisons elucidate the relationships between the universality of classificatory methods and the specific purposes served by naive and professional classification systems.
  9. Definition of the CIDOC Conceptual Reference Model (2003) 0.09 
    0.09153552 = product of:
  0.13730328 = sum of:
    0.11678971 = weight(_text_:interest in 1652) [ClassicSimilarity], result of:
      0.11678971 = score(doc=1652,freq=4.0), product of:
        0.25074318 = queryWeight, product of:
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.05046903 = queryNorm
        0.46577424 = fieldWeight in 1652, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.046875 = fieldNorm(doc=1652)
    0.02051357 = product of:
      0.04102714 = sum of:
        0.04102714 = weight(_text_:22 in 1652) [ClassicSimilarity], result of:
          0.04102714 = score(doc=1652,freq=2.0), product of:
            0.17673394 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05046903 = queryNorm
            0.23214069 = fieldWeight in 1652, 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=1652)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    This document is the formal definition of the CIDOC Conceptual Reference Model ("CRM"), a formal ontology intended to facilitate the integration, mediation and interchange of heterogeneous cultural heritage information. The CRM is the culmination of more than a decade of standards development work by the International Committee for Documentation (CIDOC) of the International Council of Museums (ICOM). Work on the CRM itself began in 1996 under the auspices of the ICOM-CIDOC Documentation Standards Working Group. Since 2000, development of the CRM has been officially delegated by ICOM-CIDOC to the CIDOC CRM Special Interest Group, which collaborates with the ISO working group ISO/TC46/SC4/WG9 to bring the CRM to the form and status of an International Standard.
    Date
    6. 8.2010 14:22:28
    Issue
    Version 3.4.9 - 30.11.2003. Produced by the ICOM/CIDOC Documentation Standards Group, continued by the CIDOC CRM Special Interest Group.
  10. Jin, Q.: Authority control in the online environment : celebrating the 20th anniversary of LITA/ALCTS CCS Authority Control in the Online Environment Interest Group (2004) 0.09 
    0.089170754 = product of:
  0.13375613 = sum of:
    0.11678971 = weight(_text_:interest in 5660) [ClassicSimilarity], result of:
      0.11678971 = score(doc=5660,freq=4.0), product of:
        0.25074318 = queryWeight, product of:
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.05046903 = queryNorm
        0.46577424 = fieldWeight in 5660, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.046875 = fieldNorm(doc=5660)
    0.016966416 = product of:
      0.03393283 = sum of:
        0.03393283 = weight(_text_:classification in 5660) [ClassicSimilarity], result of:
          0.03393283 = score(doc=5660,freq=2.0), product of:
            0.16072905 = queryWeight, product of:
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.05046903 = queryNorm
            0.21111822 = fieldWeight in 5660, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.046875 = fieldNorm(doc=5660)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    To celebrate the 20th anniversary of LITA/ALCTS CCS Authority Control in the Online Environment Interest Group (ACIG), a survey was sent out to its past chairs to identify the major issues concerning authority control during their tenure as chair, ACIG's major accomplishments during the year, and comments the past ACIG chairs had on the current focus and challenges for authority control in the future. The author discovered that since ACIG's creation in 1984 by Barbara Tillett, ACIG has contributed greatly to the field of authority control by addressing timely authority control topics with programs, discussions, and publications for the library community. ACIG meetings have always been well attended. ALL ACIG chairs were very proud to be part of having contributed to authority control and quite a few of them have been working very hard to promote authority control issues ever since.
    Source
    Cataloging and classification quarterly. 38(2004) no.2, S.xx-xx
  11. Prieto-Díaz, R.: ¬A faceted approach to building ontologies (2002) 0.09 
    0.089170754 = product of:
  0.13375613 = sum of:
    0.11678971 = weight(_text_:interest in 2259) [ClassicSimilarity], result of:
      0.11678971 = score(doc=2259,freq=4.0), product of:
        0.25074318 = queryWeight, product of:
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.05046903 = queryNorm
        0.46577424 = fieldWeight in 2259, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.046875 = fieldNorm(doc=2259)
    0.016966416 = product of:
      0.03393283 = sum of:
        0.03393283 = weight(_text_:classification in 2259) [ClassicSimilarity], result of:
          0.03393283 = score(doc=2259,freq=2.0), product of:
            0.16072905 = queryWeight, product of:
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.05046903 = queryNorm
            0.21111822 = fieldWeight in 2259, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.046875 = fieldNorm(doc=2259)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    An ontology is "an explicit conceptualization of a domain of discourse, and thus provides a shared and common understanding of the domain." We have been producing ontologies for millennia to understand and explain our rationale and environment. From Plato's philosophical framework to modern day classification systems, ontologies are, in most cases, the product of extensive analysis and categorization. Only recently has the process of building ontologies become a research topic of interest. Today, ontologies are built very much ad-hoc. A terminology is first developed providing a controlled vocabulary for the subject area or domain of interest, then it is organized into a taxonomy where key concepts are identified, and finally these concepts are defined and related to create an ontology. The intent of this paper is to show that domain analysis methods can be used for building ontologies. Domain analysis aims at generic models that represent groups of similar systems within an application domain. In this sense, it deals with categorization of common objects and operations, with clear, unambiguous definitions of them and with defining their relationships.
  12. Holley, R.P.: Are technical services topics underrepresented in the contributed papers at the ACRL national conferences? (2007) 0.09 
    0.08889113 = product of:
  0.1333367 = sum of:
    0.11919801 = weight(_text_:interest in 265) [ClassicSimilarity], result of:
      0.11919801 = score(doc=265,freq=6.0), product of:
        0.25074318 = queryWeight, product of:
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.05046903 = queryNorm
        0.47537887 = fieldWeight in 265, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.0390625 = fieldNorm(doc=265)
    0.014138679 = product of:
      0.028277358 = sum of:
        0.028277358 = weight(_text_:classification in 265) [ClassicSimilarity], result of:
          0.028277358 = score(doc=265,freq=2.0), product of:
            0.16072905 = queryWeight, product of:
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.05046903 = queryNorm
            0.17593184 = fieldWeight in 265, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.0390625 = fieldNorm(doc=265)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    This study tests the hypothesis that the contributed papers at the 12 ACRL national conferences do not cover topics of interest to technical services librarians in proportion to their membership in ACRL. The analysis showed that 14.66% of contributed papers dealt with subjects that were part of the charge of ALCTS, the technical services division in ALA, and its five sections. This percentage dropped to 7.52% with the removal of collection development papers that are also of high interest to many public services librarians. Current overlap statistics indicate that 18.83% of ACRL members also belong to ALCTS-an indication of potential ACRL member interest in technical services topics. An unexpected discovery was that the contributed papers became much more holistic with the arrival of the Internet and electronic resources in academic libraries and, starting with the 1999 Detroit national conference, were much more difficult to categorize into specialized niches. The author speculates that the attendance at the national conferences by a high proportion of librarians from small to mid-size academic libraries discourages papers on technical services topics since technical services librarians are more likely to work in large ARL libraries.
    Source
    Cataloging and classification quarterly. 44(2007) nos.3/4, S.259-269
  13. Rogers, G.P.: Roles for semantic technologies and tools in libraries (2006) 0.09 
    0.08848819 = product of:
  0.13273229 = sum of:
    0.110110395 = weight(_text_:interest in 233) [ClassicSimilarity], result of:
      0.110110395 = score(doc=233,freq=2.0), product of:
        0.25074318 = queryWeight, product of:
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.05046903 = queryNorm
        0.43913615 = fieldWeight in 233, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.0625 = fieldNorm(doc=233)
    0.022621887 = product of:
      0.045243774 = sum of:
        0.045243774 = weight(_text_:classification in 233) [ClassicSimilarity], result of:
          0.045243774 = score(doc=233,freq=2.0), product of:
            0.16072905 = queryWeight, product of:
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.05046903 = queryNorm
            0.28149095 = fieldWeight in 233, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.0625 = fieldNorm(doc=233)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    Interest is growing in Semantic technologies such as XML, XML Schema, ontologies, and ontology languages, as well as in the tools that facilitate working with such technologies. This paper examines the current library automation environment and identifies semantic tools and technologies that might be suitable for use in some libraries and other knowledge-intensive organizations.
    Source
    Cataloging and classification quarterly. 43(2006) nos.3/4, S.105-125
  14. Ewbank, L.C.; Carter, R.C.: ¬An interview with Ruth C. Carter (2007) 0.09 
    0.08848819 = product of:
  0.13273229 = sum of:
    0.110110395 = weight(_text_:interest in 251) [ClassicSimilarity], result of:
      0.110110395 = score(doc=251,freq=2.0), product of:
        0.25074318 = queryWeight, product of:
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.05046903 = queryNorm
        0.43913615 = fieldWeight in 251, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.0625 = fieldNorm(doc=251)
    0.022621887 = product of:
      0.045243774 = sum of:
        0.045243774 = weight(_text_:classification in 251) [ClassicSimilarity], result of:
          0.045243774 = score(doc=251,freq=2.0), product of:
            0.16072905 = queryWeight, product of:
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.05046903 = queryNorm
            0.28149095 = fieldWeight in 251, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.0625 = fieldNorm(doc=251)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    Ruth Carter discusses her career as a librarian, archivist, historian, and long-time editor of CCQ and other journals. Topics include her education, mentors, professional positions, work in library organizations, and interests outside of librarianship as well as trends in cataloging research, the future of cataloging, and the relations and connections among her areas of interest.
    Source
    Cataloging and classification quarterly. 44(2007) nos.1/2, S.19-38
  15. Wu, Y.-f.B.; Li, Q.; Bot, R.S.; Chen, X.: Finding nuggets in documents : a machine learning approach (2006) 0.09 
    0.08752376 = product of:
  0.13128564 = sum of:
    0.068818994 = weight(_text_:interest in 5290) [ClassicSimilarity], result of:
      0.068818994 = score(doc=5290,freq=2.0), product of:
        0.25074318 = queryWeight, product of:
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.05046903 = queryNorm
        0.27446008 = fieldWeight in 5290, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5290)
    0.062466644 = sum of:
      0.028277358 = weight(_text_:classification in 5290) [ClassicSimilarity], result of:
        0.028277358 = score(doc=5290,freq=2.0), product of:
          0.16072905 = queryWeight, product of:
            3.1847067 = idf(docFreq=4974, maxDocs=44218)
            0.05046903 = queryNorm
          0.17593184 = fieldWeight in 5290, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.1847067 = idf(docFreq=4974, maxDocs=44218)
            0.0390625 = fieldNorm(doc=5290)
      0.034189284 = weight(_text_:22 in 5290) [ClassicSimilarity], result of:
        0.034189284 = score(doc=5290,freq=2.0), product of:
          0.17673394 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05046903 = queryNorm
          0.19345059 = fieldWeight in 5290, 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=5290)
  0.6666667 = coord(2/3)
    Abstract
    Document keyphrases provide a concise summary of a document's content, offering semantic metadata summarizing a document. They can be used in many applications related to knowledge management and text mining, such as automatic text summarization, development of search engines, document clustering, document classification, thesaurus construction, and browsing interfaces. Because only a small portion of documents have keyphrases assigned by authors, and it is time-consuming and costly to manually assign keyphrases to documents, it is necessary to develop an algorithm to automatically generate keyphrases for documents. This paper describes a Keyphrase Identification Program (KIP), which extracts document keyphrases by using prior positive samples of human identified phrases to assign weights to the candidate keyphrases. The logic of our algorithm is: The more keywords a candidate keyphrase contains and the more significant these keywords are, the more likely this candidate phrase is a keyphrase. KIP's learning function can enrich the glossary database by automatically adding new identified keyphrases to the database. KIP's personalization feature will let the user build a glossary database specifically suitable for the area of his/her interest. The evaluation results show that KIP's performance is better than the systems we compared to and that the learning function is effective.
    Date
    22. 7.2006 17:25:48
  16. Lam, W.; Mostafa, J.: Modeling user interest shift using a Baysian approach (2001) 0.08 
    0.084969714 = product of:
  0.25490913 = sum of:
    0.25490913 = weight(_text_:interest in 2658) [ClassicSimilarity], result of:
      0.25490913 = score(doc=2658,freq=14.0), product of:
        0.25074318 = queryWeight, product of:
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.05046903 = queryNorm
        1.0166144 = fieldWeight in 2658, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2658)
  0.33333334 = coord(1/3)
    Abstract
    We investigate the modeling of changes in user interest in information filtering systems. A new technique for tracking user interest shifts based on a Bayesian approach is developed. The interest tracker is integrated into a profile learning module of a filtering system. We present an analytical study to establish the rate of convergence for the profile learning with and without the user interest tracking component. We examine the relationship among degree of shift, cost of detection error, and time needed for detection. To study the effect of different patterns of interest shift on system performance we also conducted several filtering experiments. Generally, the findings show that the Bayesian approach is a feasible and effective technique for modeling user interest shift
  17. Goodrum, A.A.; Rorvig, M.E.; Jeong, K.-T.; Suresh, C.: ¬An open source agenda for research linking text and image content features (2001) 0.08 
    0.08289318 = product of:
  0.12433976 = sum of:
    0.0963466 = weight(_text_:interest in 6533) [ClassicSimilarity], result of:
      0.0963466 = score(doc=6533,freq=2.0), product of:
        0.25074318 = queryWeight, product of:
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.05046903 = queryNorm
        0.38424414 = fieldWeight in 6533, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.0546875 = fieldNorm(doc=6533)
    0.027993156 = product of:
      0.05598631 = sum of:
        0.05598631 = weight(_text_:classification in 6533) [ClassicSimilarity], result of:
          0.05598631 = score(doc=6533,freq=4.0), product of:
            0.16072905 = queryWeight, product of:
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.05046903 = queryNorm
            0.34832728 = fieldWeight in 6533, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.0546875 = fieldNorm(doc=6533)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    The use of primitive content features of images for classification and retrieval has matured over the past decade. However, human beings often prefer to locate images using words. This article proposes a number of methods to utilize image primitives to support term assignment for image classification. Further, the authors propose to release code for image analysis in a common tool set for other researchers to use. Of particular interest to the authors is the expansion of work by researchers in image indexing to include image content based feature extraction capabilities in their work
  18. LaBarre, K.: Adventures in faceted classification: a brave new world or a world of confusion? (2004) 0.08 
    0.08289318 = product of:
  0.12433976 = sum of:
    0.0963466 = weight(_text_:interest in 2634) [ClassicSimilarity], result of:
      0.0963466 = score(doc=2634,freq=2.0), product of:
        0.25074318 = queryWeight, product of:
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.05046903 = queryNorm
        0.38424414 = fieldWeight in 2634, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2634)
    0.027993156 = product of:
      0.05598631 = sum of:
        0.05598631 = weight(_text_:classification in 2634) [ClassicSimilarity], result of:
          0.05598631 = score(doc=2634,freq=4.0), product of:
            0.16072905 = queryWeight, product of:
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.05046903 = queryNorm
            0.34832728 = fieldWeight in 2634, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2634)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    A preliminary, purposive survey of definitions and current applications of facet analytical theory (FA) is used to develop a framework for the analysis of Websites. This set of guidelines may well serve to highlight commonalities and differences among FA applications an the Web. Rather than identifying FA as the terrain of a particular interest group, the goal is to explore current practices, uncover common misconceptions, extend understanding, and highlight developments that augment the traditional practice of FA and faceted classification (FC).
  19. Sebastiani, F.: Classification of text, automatic (2006) 0.08 
    0.08289318 = product of:
  0.12433976 = sum of:
    0.0963466 = weight(_text_:interest in 5003) [ClassicSimilarity], result of:
      0.0963466 = score(doc=5003,freq=2.0), product of:
        0.25074318 = queryWeight, product of:
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.05046903 = queryNorm
        0.38424414 = fieldWeight in 5003, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5003)
    0.027993156 = product of:
      0.05598631 = sum of:
        0.05598631 = weight(_text_:classification in 5003) [ClassicSimilarity], result of:
          0.05598631 = score(doc=5003,freq=4.0), product of:
            0.16072905 = queryWeight, product of:
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.05046903 = queryNorm
            0.34832728 = fieldWeight in 5003, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.0546875 = fieldNorm(doc=5003)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    Automatic text classification (ATC) is a discipline at the crossroads of information retrieval (IR), machine learning (ML), and computational linguistics (CL), and consists in the realization of text classifiers, i.e. software systems capable of assigning texts to one or more categories, or classes, from a predefined set. Applications range from the automated indexing of scientific articles, to e-mail routing, spam filtering, authorship attribution, and automated survey coding. This article will focus on the ML approach to ATC, whereby a software system (called the learner) automatically builds a classifier for the categories of interest by generalizing from a "training" set of pre-classified texts.
  20. Araghi, G.F.: ¬A dynamic look toward classification and retrieval (2004) 0.08 
    0.08276124 = product of:
  0.12414186 = sum of:
    0.082582794 = weight(_text_:interest in 5530) [ClassicSimilarity], result of:
      0.082582794 = score(doc=5530,freq=2.0), product of:
        0.25074318 = queryWeight, product of:
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.05046903 = queryNorm
        0.3293521 = fieldWeight in 5530, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.9682584 = idf(docFreq=835, maxDocs=44218)
          0.046875 = fieldNorm(doc=5530)
    0.041559063 = product of:
      0.083118126 = sum of:
        0.083118126 = weight(_text_:classification in 5530) [ClassicSimilarity], result of:
          0.083118126 = score(doc=5530,freq=12.0), product of:
            0.16072905 = queryWeight, product of:
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.05046903 = queryNorm
            0.5171319 = fieldWeight in 5530, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.046875 = fieldNorm(doc=5530)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    In this article the relationship between classification/indexing and retrieval is discussed. In library and information science, classification and retrieval have always been closely associated with each other. But in certain ages, because of a lack of interest in applying knowledge, it was thought that libraries were just a place for gathering and keeping books and other documents as assets. And therefore, people thought that classification was simply for arrangement, in order to have a kind of system for objects that they considered to be luxuries. The reason for this lies in their static view of things, including libraries. Changing attitudes and having a dynamic view of the world of reality will change everything. Thus, if we define that the library is not only a place for book collection but is a place where people fill their information needs, and also that librarianship is not mainly about classification, but is a discipline by which we retrieve information and receive knowledge, we may see a great change in the retrieval process.
    Source
    Cataloging and classification quarterly. 38(2004) no.1, S.43-53

Authors

Languages

Types

  • a 2339
  • m 276
  • el 170
  • s 103
  • b 30
  • x 21
  • i 10
  • r 8
  • p 6
  • n 5
  • More… Less…

Themes

Subjects

Classifications