Search (5 results, page 1 of 1)

0.0068306234 = product of:
  0.027322493 = sum of:
    0.027322493 = product of:
      0.054644987 = sum of:
        0.054644987 = weight(_text_:22 in 1484) [ClassicSimilarity], result of:
          0.054644987 = score(doc=1484,freq=2.0), product of:
            0.17654699 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050415643 = queryNorm
            0.30952093 = fieldWeight in 1484, 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=1484)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

13. 9.2014 14:45:22

0.005757545 = product of:
  0.02303018 = sum of:
    0.02303018 = weight(_text_:information in 2799) [ClassicSimilarity], result of:
      0.02303018 = score(doc=2799,freq=10.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.2602176 = fieldWeight in 2799, 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=2799)
  0.25 = coord(1/4)

Abstract

With ever increasing information being available to the end users, search engines have become the most powerful tools for obtaining useful information scattered on the Web. However, it is very common that even most renowned search engines return result sets with not so useful pages to the user. Research on semantic search aims to improve traditional information search and retrieval methods where the basic relevance criteria rely primarily on the presence of query keywords within the returned pages. This work is an attempt to explore different relevancy ranking approaches based on semantics which are considered appropriate for the retrieval of relevant information. In this paper, various pilot projects and their corresponding outcomes have been investigated based on methodologies adopted and their most distinctive characteristics towards ranking. An overview of selected approaches and their comparison by means of the classification criteria has been presented. With the help of this comparison, some common concepts and outstanding features have been identified.

Source

Information processing and management. 50(2014) no.2, S.416-425

0.0042914203 = product of:
  0.017165681 = sum of:
    0.017165681 = weight(_text_:information in 928) [ClassicSimilarity], result of:
      0.017165681 = score(doc=928,freq=2.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.19395474 = fieldWeight in 928, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.078125 = fieldNorm(doc=928)
  0.25 = coord(1/4)

Abstract

In this note, we show how to marginalize over the damping parameter of the PageRank equation so as to obtain a parameter-free version known as TotalRank. Our discussion is meant as a reference and intended to provide a guided tour towards an interesting result that has applications in information retrieval and classification.

0.0034331365 = product of:
  0.013732546 = sum of:
    0.013732546 = weight(_text_:information in 393) [ClassicSimilarity], result of:
      0.013732546 = score(doc=393,freq=8.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.1551638 = fieldWeight in 393, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.03125 = fieldNorm(doc=393)
  0.25 = coord(1/4)

Abstract

This study employed benchmarking and intellectual relevance judgment in evaluating Google, Yahoo!, Bing, Yahoo! Kids, and Ask Kids on 30 queries that children formulated to find information for specific tasks. Retrieved hits on given queries were benchmarked to Google's and Yahoo! Kids' top-five ranked hits retrieved. Relevancy of hits was judged on a graded scale; precision was calculated using the precision-at-ten metric (P@10). Yahoo! and Bing produced a similar percentage in hit overlap with Google (nearly 30%), but differed in the ranking of hits. Ask Kids retrieved 11% in hit overlap with Google versus 3% by Yahoo! Kids. The engines retrieved 26 hits across query clusters that overlapped with Yahoo! Kids' top-five ranked hits. Precision (P) that the engines produced across the queries was P = 0.48 for relevant hits, and P = 0.28 for partially relevant hits. Precision by Ask Kids was P = 0.44 for relevant hits versus P = 0.21 by Yahoo! Kids. Bing produced the highest total precision (TP) of relevant hits (TP = 0.86) across the queries, and Yahoo! Kids yielded the lowest (TP = 0.47). Average precision (AP) of relevant hits was AP = 0.56 by leading engines versus AP = 0.29 by small engines. In contrast, average precision of partially relevant hits was AP = 0.83 by small engines versus AP = 0.33 by leading engines. Average precision of relevant hits across the engines was highest on two-word queries and lowest on one-word queries. Google performed best on natural language queries; Bing did the same (P = 0.69) on two-word queries. The findings have implications for search engine ranking algorithms, relevance theory, search engine design, research design, and information literacy.

Source

Journal of the American Society for Information Science and Technology. 63(2012) no.9, S.1879-1896

0.0030039945 = product of:
  0.012015978 = sum of:
    0.012015978 = weight(_text_:information in 5023) [ClassicSimilarity], result of:
      0.012015978 = score(doc=5023,freq=2.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.13576832 = fieldWeight in 5023, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5023)
  0.25 = coord(1/4)

Source

Information - Wissenschaft und Praxis. 70(2019) H.1, S.14-23