Search (3 results, page 1 of 1)

0.0149546135 = product of:
  0.04486384 = sum of:
    0.037119392 = weight(_text_:retrieval in 6492) [ClassicSimilarity], result of:
      0.037119392 = score(doc=6492,freq=6.0), product of:
        0.12824841 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.042397358 = queryNorm
        0.28943354 = fieldWeight in 6492, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=6492)
    0.007744446 = product of:
      0.023233337 = sum of:
        0.023233337 = weight(_text_:system in 6492) [ClassicSimilarity], result of:
          0.023233337 = score(doc=6492,freq=2.0), product of:
            0.13353272 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.042397358 = queryNorm
            0.17398985 = fieldWeight in 6492, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0390625 = fieldNorm(doc=6492)
      0.33333334 = coord(1/3)
  0.33333334 = coord(2/6)

Abstract

This research presents an algorithmic approach to addressing the vocabulary problem in scientific information retrieval and information sharing, using the molecular biology domain as an example. We first present a literature review of cognitive studies related to the vocabulary problem and vocabulary-based search aids (thesauri) and then discuss techniques for building robust and domain-specific thesauri to assist in cross-domain scientific information retrieval. Using a variation of the automatic thesaurus generation techniques, which we refer to as the concept space approach, we recently conducted an experiment in the molecular biology domain in which we created a C. elegans worm thesaurus of 7.657 worm-specific terms and a Drosophila fly thesaurus of 15.626 terms. About 30% of these terms overlapped, which created vocabulary paths from one subject domain to the other. Based on a cognitve study of term association involving 4 biologists, we found that a large percentage (59,6-85,6%) of the terms suggested by the subjects were identified in the cojoined fly-worm thesaurus. However, we found only a small percentage (8,4-18,1%) of the associations suggested by the subjects in the thesaurus

0.012953277 = product of:
  0.03885983 = sum of:
    0.025717068 = weight(_text_:retrieval in 5202) [ClassicSimilarity], result of:
      0.025717068 = score(doc=5202,freq=2.0), product of:
        0.12824841 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.042397358 = queryNorm
        0.20052543 = fieldWeight in 5202, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=5202)
    0.01314276 = product of:
      0.03942828 = sum of:
        0.03942828 = weight(_text_:system in 5202) [ClassicSimilarity], result of:
          0.03942828 = score(doc=5202,freq=4.0), product of:
            0.13353272 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.042397358 = queryNorm
            0.29527056 = fieldWeight in 5202, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.046875 = fieldNorm(doc=5202)
      0.33333334 = coord(1/3)
  0.33333334 = coord(2/6)

Abstract

In this article, we report research on an algorithmic approach to alleviating search uncertainty in a large information space. Grounded on object filtering, automatic indexing, and co-occurence analysis, we performed a large-scale experiment using a parallel supercomputer (SGI Power Challenge) to analyze 400.000+ abstracts in an INSPEC computer engineering collection. Two system-generated thesauri, one based on a combined object filtering and automatic indexing method, and the other based on automatic indexing only, were compaed with the human-generated INSPEC subject thesaurus. Our user evaluation revealed that the system-generated thesauri were better than the INSPEC thesaurus in 'concept recall', but in 'concept precision' the 3 thesauri were comparable. Our analysis also revealed that the terms suggested by the 3 thesauri were complementary and could be used to significantly increase 'variety' in search terms the thereby reduce search uncertainty

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.011429808 = product of:
  0.06857885 = sum of:
    0.06857885 = weight(_text_:retrieval in 8549) [ClassicSimilarity], result of:
      0.06857885 = score(doc=8549,freq=2.0), product of:
        0.12824841 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.042397358 = queryNorm
        0.5347345 = fieldWeight in 8549, 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=8549)
  0.16666667 = coord(1/6)