Search (6 results, page 1 of 1)

0.12821087 = product of:
  0.1923163 = sum of:
    0.0929755 = weight(_text_:search in 5116) [ClassicSimilarity], result of:
      0.0929755 = score(doc=5116,freq=6.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.5321022 = fieldWeight in 5116, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0625 = fieldNorm(doc=5116)
    0.099340804 = product of:
      0.19868161 = sum of:
        0.19868161 = weight(_text_:engines in 5116) [ClassicSimilarity], result of:
          0.19868161 = score(doc=5116,freq=6.0), product of:
            0.25542772 = queryWeight, product of:
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.05027291 = queryNorm
            0.7778389 = fieldWeight in 5116, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.0625 = fieldNorm(doc=5116)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

There are at least three possible ways that documents are distributed by relevance: informetric (power law), inverse logistic, and dichotomous. The nature of the type of distribution has implications for the construction of relevance ranking algorithms for search engines, for automated (blind) relevance feedback, for user behavior when using Web search engines, for combining of outputs of search engines for metasearch, for topic detection and tracking, and for the methodology of evaluation of information retrieval systems.

0.05552859 = product of:
  0.08329288 = sum of:
    0.04744636 = weight(_text_:search in 2784) [ClassicSimilarity], result of:
      0.04744636 = score(doc=2784,freq=4.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.27153727 = fieldWeight in 2784, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2784)
    0.03584652 = product of:
      0.07169304 = sum of:
        0.07169304 = weight(_text_:engines in 2784) [ClassicSimilarity], result of:
          0.07169304 = score(doc=2784,freq=2.0), product of:
            0.25542772 = queryWeight, product of:
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.05027291 = queryNorm
            0.2806784 = fieldWeight in 2784, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2784)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Dealing with information is one of the vital skills in the 21st century. It takes a fair degree of information savvy to create, represent and supply information as well as to search for and retrieve relevant knowledge. How does information (documents, pieces of knowledge) have to be organized in order to be retrievable? What role does metadata play? What are search engines on the Web, or in corporate intranets, and how do they work? How must one deal with natural language processing and tools of knowledge organization, such as thesauri, classification systems, and ontologies? How useful is social tagging? How valuable are intellectually created abstracts and automatically prepared extracts? Which empirical methods allow for user research and which for the evaluation of information systems? This Handbook is a basic work of information science, providing a comprehensive overview of the current state of information retrieval and knowledge representation. It addresses readers from all professions and scientific disciplines, but particularly scholars, practitioners and students of Information Science, Library Science, Computer Science, Information Management, and Knowledge Management. This Handbook is a suitable reference work for Public and Academic Libraries.

0.035505608 = product of:
  0.10651682 = sum of:
    0.10651682 = weight(_text_:search in 210) [ClassicSimilarity], result of:
      0.10651682 = score(doc=210,freq=14.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.6095997 = fieldWeight in 210, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.046875 = fieldNorm(doc=210)
  0.33333334 = coord(1/3)

Abstract

After modeling expert user needs with regard to intellectual property information, we analyze and compare the main providers in this specific information area (Thomson DIALOG, Esp@cenet by the European Patent Office, Questel-Orbit, and STN International) in terms of system content and system functionality. The key question is whether the main providers are able to satisfy these expert user needs. For patent information, some special retrieval features such as chemical structure search (including Markush search), patent family references and citations search, biosequence search, and basic informetric functionality such as ranking, mapping, and visualization of information flows are realized. Considering the results of information science research, the practice of patent information shows unexhausted improvement opportunities (e.g., the application of bibliographic patent coupling and co-patent-citation for mapping patents, patent assignees, and technology specialties). For trademark search, users need multiple truncated search (realized) as well as phonetic search and image retrieval (not realized yet).

0.025006426 = product of:
  0.07501928 = sum of:
    0.07501928 = weight(_text_:search in 865) [ClassicSimilarity], result of:
      0.07501928 = score(doc=865,freq=10.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.4293381 = fieldWeight in 865, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=865)
  0.33333334 = coord(1/3)

Abstract

Purpose - Many Web 2.0 services (including Library 2.0 catalogs) make use of folksonomies. The purpose of this paper is to cut off all tags in the long tail of a document-specific tag distribution. The remaining tags at the beginning of a tag distribution are considered power tags and form a new, additional search option in information retrieval systems. Design/methodology/approach - In a theoretical approach the paper discusses document-specific tag distributions (power law and inverse-logistic shape), the development of such distributions (Yule-Simon process and shuffling theory) and introduces search tags (besides the well-known index tags) as a possibility for generating tag distributions. Findings - Search tags are compatible with broad and narrow folksonomies and with all knowledge organization systems (e.g. classification systems and thesauri), while index tags are only applicable in broad folksonomies. Based on these findings, the paper presents a sketch of an algorithm for mining and processing power tags in information retrieval systems. Research limitations/implications - This conceptual approach is in need of empirical evaluation in a concrete retrieval system. Practical implications - Power tags are a new search option for retrieval systems to limit the amount of hits. Originality/value - The paper introduces power tags as a means for enhancing the precision of search results in information retrieval systems that apply folksonomies, e.g. catalogs in Library 2.0environments.

0.011183213 = product of:
  0.03354964 = sum of:
    0.03354964 = weight(_text_:search in 2792) [ClassicSimilarity], result of:
      0.03354964 = score(doc=2792,freq=2.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.19200584 = fieldWeight in 2792, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2792)
  0.33333334 = coord(1/3)

Abstract

Some documents provoke emotions in people viewing them. Will it be possible to describe emotions consistently and use this information in retrieval systems? We tested collective (statistically aggregated) emotion indexing using images as examples. Considering psychological results, basic emotions are anger, disgust, fear, happiness, and sadness. This study follows an approach developed by Lee and Neal (2007) for music emotion retrieval and applies scroll bars for tagging basic emotions and their intensities. A sample comprising 763 persons tagged emotions caused by images (retrieved from www.Flickr.com) applying scroll bars and (linguistic) tags. Using SPSS, we performed descriptive statistics and correlation analysis. For more than half of the images, the test persons have clear emotion favorites. There are prototypical images for given emotions. The document-specific consistency of tagging using a scroll bar is, for some images, very high. Most of the (most commonly used) linguistic tags are on the basic level (in the sense of Rosch's basic level theory). The distributions of the linguistic tags in our examples follow an inverse power-law. Hence, it seems possible to apply collective image emotion tagging to image information systems and to present a new search option for basic emotions. This article is one of the first steps in the research area of emotional information retrieval (EmIR).

0.0056760716 = product of:
  0.017028214 = sum of:
    0.017028214 = product of:
      0.03405643 = sum of:
        0.03405643 = weight(_text_:22 in 4452) [ClassicSimilarity], result of:
          0.03405643 = score(doc=4452,freq=2.0), product of:
            0.17604718 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05027291 = queryNorm
            0.19345059 = fieldWeight in 4452, 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=4452)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Date

3. 7.2011 19:22:49