Search (9 results, page 1 of 1)

0.01975197 = product of:
  0.092175856 = sum of:
    0.022830293 = weight(_text_:information in 7782) [ClassicSimilarity], result of:
      0.022830293 = score(doc=7782,freq=4.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.43886948 = fieldWeight in 7782, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.125 = fieldNorm(doc=7782)
    0.047932718 = weight(_text_:retrieval in 7782) [ClassicSimilarity], result of:
      0.047932718 = score(doc=7782,freq=2.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.5347345 = fieldWeight in 7782, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.125 = fieldNorm(doc=7782)
    0.021412853 = product of:
      0.064238556 = sum of:
        0.064238556 = weight(_text_:22 in 7782) [ClassicSimilarity], result of:
          0.064238556 = score(doc=7782,freq=2.0), product of:
            0.103770934 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.029633347 = queryNorm
            0.61904186 = fieldWeight in 7782, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.125 = fieldNorm(doc=7782)
      0.33333334 = coord(1/3)
  0.21428572 = coord(3/14)

Source

Encyclopedia of library and information science. Vol.59, [=Suppl.22]

0.019570844 = product of:
  0.0913306 = sum of:
    0.041844364 = weight(_text_:web in 3268) [ClassicSimilarity], result of:
      0.041844364 = score(doc=3268,freq=8.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.43268442 = fieldWeight in 3268, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=3268)
    0.013536699 = weight(_text_:information in 3268) [ClassicSimilarity], result of:
      0.013536699 = score(doc=3268,freq=10.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.2602176 = fieldWeight in 3268, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=3268)
    0.03594954 = weight(_text_:retrieval in 3268) [ClassicSimilarity], result of:
      0.03594954 = score(doc=3268,freq=8.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.40105087 = fieldWeight in 3268, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=3268)
  0.21428572 = coord(3/14)

Abstract

The PageRank method is used by the Google Web search engine to compute the importance of Web pages. Two different views have been developed for the Interpretation of the PageRank method and values: (a) stochastic (random surfer): the PageRank values can be conceived as the steady-state distribution of a Markov chain, and (b) algebraic: the PageRank values form the eigenvector corresponding to eigenvalue 1 of the Web link matrix. The Interaction Information Retrieval (1**2 R) method is a nonclassical information retrieval paradigm, which represents a connectionist approach based an dynamic systems. In the present paper, a different Interpretation of PageRank is proposed, namely, a dynamic systems viewpoint, by showing that the PageRank method can be formally interpreted as a particular case of the Interaction Information Retrieval method; and thus, the PageRank values may be interpreted as neutral equilibrium points of the Web.

Source

Journal of the American Society for Information Science and Technology. 56(2005) no.1, S.63-69

0.014333045 = product of:
  0.06688754 = sum of:
    0.01482871 = weight(_text_:information in 1451) [ClassicSimilarity], result of:
      0.01482871 = score(doc=1451,freq=12.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.2850541 = fieldWeight in 1451, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=1451)
    0.044029012 = weight(_text_:retrieval in 1451) [ClassicSimilarity], result of:
      0.044029012 = score(doc=1451,freq=12.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.49118498 = fieldWeight in 1451, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=1451)
    0.008029819 = product of:
      0.024089456 = sum of:
        0.024089456 = weight(_text_:22 in 1451) [ClassicSimilarity], result of:
          0.024089456 = score(doc=1451,freq=2.0), product of:
            0.103770934 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.029633347 = queryNorm
            0.23214069 = fieldWeight in 1451, 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=1451)
      0.33333334 = coord(1/3)
  0.21428572 = coord(3/14)

Abstract

Research an the use of mathematical, logical, and formal methods, has been central to Information Retrieval research for a long time. Research in this area is important not only because it helps enhancing retrieval effectiveness, but also because it helps clarifying the underlying concepts of Information Retrieval. In this article we outline some of the major aspects of the subject, and summarize the papers of this special issue with respect to how they relate to these aspects. We conclude by highlighting some directions of future research, which are needed to better understand the formal characteristics of Information Retrieval.

Date

22. 3.2003 19:27:36

Footnote

Einführung zu den Beiträgen eines Themenheftes: Mathematical, logical, and formal methods in information retrieval

Source

Journal of the American Society for Information Science and technology. 54(2003) no.4, S.281-284

0.0112591665 = product of:
  0.052542776 = sum of:
    0.012357258 = weight(_text_:information in 1753) [ClassicSimilarity], result of:
      0.012357258 = score(doc=1753,freq=12.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.23754507 = fieldWeight in 1753, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1753)
    0.033494003 = weight(_text_:retrieval in 1753) [ClassicSimilarity], result of:
      0.033494003 = score(doc=1753,freq=10.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.37365708 = fieldWeight in 1753, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1753)
    0.0066915164 = product of:
      0.020074548 = sum of:
        0.020074548 = weight(_text_:22 in 1753) [ClassicSimilarity], result of:
          0.020074548 = score(doc=1753,freq=2.0), product of:
            0.103770934 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.029633347 = queryNorm
            0.19345059 = fieldWeight in 1753, 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=1753)
      0.33333334 = coord(1/3)
  0.21428572 = coord(3/14)

Abstract

This book offers a comprehensive and consistent mathematical approach to information retrieval (IR) without which no implementation is possible, and sheds an entirely new light upon the structure of IR models. It contains the descriptions of all IR models in a unified formal style and language, along with examples for each, thus offering a comprehensive overview of them. The book also creates mathematical foundations and a consistent mathematical theory (including all mathematical results achieved so far) of IR as a stand-alone mathematical discipline, which thus can be read and taught independently. Also, the book contains all necessary mathematical knowledge on which IR relies, to help the reader avoid searching different sources. The book will be of interest to computer or information scientists, librarians, mathematicians, undergraduate students and researchers whose work involves information retrieval.

Date

22. 3.2008 12:26:32

LCSH

Information storage and retrieval

Subject

Information storage and retrieval

0.010258756 = product of:
  0.07181129 = sum of:
    0.020970963 = weight(_text_:information in 110) [ClassicSimilarity], result of:
      0.020970963 = score(doc=110,freq=6.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.40312737 = fieldWeight in 110, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.09375 = fieldNorm(doc=110)
    0.050840326 = weight(_text_:retrieval in 110) [ClassicSimilarity], result of:
      0.050840326 = score(doc=110,freq=4.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.5671716 = fieldWeight in 110, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.09375 = fieldNorm(doc=110)
  0.14285715 = coord(2/14)

Footnote

Einführung in einen thematischen Schwerpunkt "Formal Methods for Information Retrieval"

Source

Information processing and management. 42(2006) no.1, S.1-3

0.009353022 = product of:
  0.06547115 = sum of:
    0.009988253 = weight(_text_:information in 8157) [ClassicSimilarity], result of:
      0.009988253 = score(doc=8157,freq=4.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.1920054 = fieldWeight in 8157, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=8157)
    0.055482898 = weight(_text_:retrieval in 8157) [ClassicSimilarity], result of:
      0.055482898 = score(doc=8157,freq=14.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.61896384 = fieldWeight in 8157, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0546875 = fieldNorm(doc=8157)
  0.14285715 = coord(2/14)

Abstract

In existing information retrieval models there are three different ways documents are represented for retrieval purposes: vectors of weights, collections of sentences and artificial neurons. Accordingly, retrieval depends on a similarity function, or means an inference, or is a spreading of activation. Relevancy is considered to be a critical modelling parameter which is either a priori or it is not treated at all. Assuming that relevancy may equally be an emergent entity, thus not requiring any a priori modelling, the paper proposes the Interaction Informatzion Retrieval model in which documents are interconnected, queries and documents are treated in the same way, and in which retrieval is the result of the interconnection between query and documents. Algorithms and experiences gained with practical applications are presented. A theoretical mathematical formulation of this type of retrieval is also given

0.008548964 = product of:
  0.059842743 = sum of:
    0.017475804 = weight(_text_:information in 4768) [ClassicSimilarity], result of:
      0.017475804 = score(doc=4768,freq=6.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.3359395 = fieldWeight in 4768, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.078125 = fieldNorm(doc=4768)
    0.042366937 = weight(_text_:retrieval in 4768) [ClassicSimilarity], result of:
      0.042366937 = score(doc=4768,freq=4.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.47264296 = fieldWeight in 4768, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.078125 = fieldNorm(doc=4768)
  0.14285715 = coord(2/14)

Abstract

A unified mathematical definition for the classical (vector and probabilistic) models of information retrieval is given. Also, a methematical structure (Diophantine set) behind relevance feedback is identified

Source

Journal of the American Society for Information Science. 51(2000) no.7, S.614-624

0.008208191 = product of:
  0.05745733 = sum of:
    0.010089659 = weight(_text_:information in 2237) [ClassicSimilarity], result of:
      0.010089659 = score(doc=2237,freq=8.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.19395474 = fieldWeight in 2237, 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=2237)
    0.047367673 = weight(_text_:retrieval in 2237) [ClassicSimilarity], result of:
      0.047367673 = score(doc=2237,freq=20.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.5284309 = fieldWeight in 2237, 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=2237)
  0.14285715 = coord(2/14)

Abstract

The concepts of Shannon information and entropy have been applied to a number of information retrieval tasks such as to formalize the probabilistic model, to design practical retrieval systems, to cluster documents, and to model texture in image retrieval. In this report, the concept of entropy is used for a different purpose. It is shown that any positive Retrieval Status Value (RSV)based retrieval system may be conceived as a special probability space in which the amount of the associated Shannon information is being reduced; in this view, the retrieval system is referred to as Uncertainty Decreasing Operation (UDO). The concept of UDO is then proposed as a theoretical background for term and query discrimination Power, and it is applied to the computation of term and query discrimination values in the vector space retrieval model. Experimental evidence is given as regards such computation; the results obtained compare weIl to those obtained using vector-based calculation of term discrimination values. The UDO-based computation, however, presents advantages over the vectorbased calculation: It is faster, easier to assess and handle in practice, and its application is not restricted to the vector space model. Based an the ADI test collection, it is shown that the UDO-based Term Discrimination Value (TDV) weighting scheme yields better retrieval effectiveness than using the vector-based TDV weighting scheme. Also, experimental evidence is given to the intuition that the choice of an appropriate weighting scheure and similarity measure depends an collection properties, and thus the UDO approach may be used as a theoretical basis for this intuition.

Source

Journal of the American Society for Information Science and technology. 55(2004) no.7, S.613-626

0.006825707 = product of:
  0.047779948 = sum of:
    0.009885807 = weight(_text_:information in 445) [ClassicSimilarity], result of:
      0.009885807 = score(doc=445,freq=12.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.19003606 = fieldWeight in 445, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.03125 = fieldNorm(doc=445)
    0.03789414 = weight(_text_:retrieval in 445) [ClassicSimilarity], result of:
      0.03789414 = score(doc=445,freq=20.0), product of:
        0.08963835 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.029633347 = queryNorm
        0.42274472 = fieldWeight in 445, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.03125 = fieldNorm(doc=445)
  0.14285715 = coord(2/14)

Abstract

The vector space model of information retrieval is one of the classical and widely applied retrieval models. Paradoxically, it has been characterized by a discrepancy between its formal framework and implementable form. The underlying concepts of the vector space model are mathematical terms: linear space, vector, and inner product. However, in the vector space model, the mathematical meaning of these concepts is not preserved. They are used as mere computational constructs or metaphors. Thus, the vector space model actually does not follow formally from the mathematical concepts on which it has been claimed to rest. This problem has been recognized for more than two decades, but no proper solution has emerged so far. The present article proposes a solution to this problem. First, the concept of retrieval is defined based on the mathematical measure theory. Then, retrieval is particularized using fuzzy set theory. As a result, the retrieval function is conceived as the cardinality of the intersection of two fuzzy sets. This view makes it possible to build a connection to linear spaces. It is shown that the classical and the generalized vector space models, as well as the latent semantic indexing model, gain a correct formal background with which they are consistent. At the same time it becomes clear that the inner product is not a necessary ingredient of the vector space model, and hence of Information Retrieval (IR). The Principle of Object Invariance is introduced to handle this situation. Moreover, this view makes it possible to consistently formulate new retrieval methods: in linear space with general basis, entropy-based, and probability-based. It is also shown that Information Retrieval may be viewed as integral calculus, and thus it gains a very compact and elegant mathematical way of writing. Also, Information Retrieval may thus be conceived as an application of mathematical measure theory.

Source

Journal of the American Society for Information Science and Technology. 58(2007) no.8, S.1108-1122