Search (3 results, page 1 of 1)

  • × author_ss:"Chen, L."
  • × year_i:[2010 TO 2020}
  1. Tang, X.; Chen, L.; Cui, J.; Wei, B.: Knowledge representation learning with entity descriptions, hierarchical types, and textual relations (2019) 0.04 
    0.037439607 = product of:
  0.074879214 = sum of:
    0.074879214 = sum of:
      0.037876792 = weight(_text_:b in 5101) [ClassicSimilarity], result of:
        0.037876792 = score(doc=5101,freq=2.0), product of:
          0.16126883 = queryWeight, product of:
            3.542962 = idf(docFreq=3476, maxDocs=44218)
            0.045518078 = queryNorm
          0.23486741 = fieldWeight in 5101, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.542962 = idf(docFreq=3476, maxDocs=44218)
            0.046875 = fieldNorm(doc=5101)
      0.037002426 = weight(_text_:22 in 5101) [ClassicSimilarity], result of:
        0.037002426 = score(doc=5101,freq=2.0), product of:
          0.15939656 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.045518078 = queryNorm
          0.23214069 = fieldWeight in 5101, 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=5101)
  0.5 = coord(1/2)
    Date
    17. 3.2019 13:22:53
  2. Han, B.; Chen, L.; Tian, X.: Knowledge based collection selection for distributed information retrieval (2018) 0.01 
    0.007890998 = product of:
  0.015781997 = sum of:
    0.015781997 = product of:
      0.031563994 = sum of:
        0.031563994 = weight(_text_:b in 3289) [ClassicSimilarity], result of:
          0.031563994 = score(doc=3289,freq=2.0), product of:
            0.16126883 = queryWeight, product of:
              3.542962 = idf(docFreq=3476, maxDocs=44218)
              0.045518078 = queryNorm
            0.19572285 = fieldWeight in 3289, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.542962 = idf(docFreq=3476, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3289)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
  3. Chen, L.; Holsapple, C.W.; Hsiao, S.-H.; Ke, Z.; Oh, J.-Y.; Yang, Z.: Knowledge-dissemination channels : analytics of stature evaluation (2017) 0.01 
    0.007890998 = product of:
  0.015781997 = sum of:
    0.015781997 = product of:
      0.031563994 = sum of:
        0.031563994 = weight(_text_:b in 3531) [ClassicSimilarity], result of:
          0.031563994 = score(doc=3531,freq=2.0), product of:
            0.16126883 = queryWeight, product of:
              3.542962 = idf(docFreq=3476, maxDocs=44218)
              0.045518078 = queryNorm
            0.19572285 = fieldWeight in 3531, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.542962 = idf(docFreq=3476, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3531)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Understanding relative statures of channels for disseminating knowledge is of practical interest to both generators and consumers of knowledge flows. For generators, stature can influence attractiveness of alternative dissemination routes and deliberations of those who assess generator performance. For knowledge consumers, channel stature may influence knowledge content to which they are exposed. This study introduces a novel approach to conceptualizing and measuring stature of knowledge-dissemination channels: the power-impact (PI) technique. It is a flexible technique having 3 complementary variants, giving holistic insights about channel stature by accounting for both attraction of knowledge generators to a distribution channel and degree to which knowledge consumers choose to use a channel's knowledge content. Each PI variant is expressed in terms of multiple parameters, permitting customization of stature evaluation to suit its user's preferences. In the spirit of analytics, each PI variant is driven by objective evidence of actual behaviors. The PI technique is based on 2 building blocks: (a) power that channels have for attracting results of generators' knowledge work, and (b) impact that channel contents' exhibit on prospective recipients. Feasibility and functionality of the PI-technique design are demonstrated by applying it to solve a problem of journal stature evaluation for the information-systems discipline.

Authors