Search (5 results, page 1 of 1)

  • × theme_ss:"Informetrie"
  • × author_ss:"Song, M."
  1. Lee, K.; Kim, S.Y.; Kim, E.H.-J.; Song, M.: Comparative evaluation of bibliometric content networks by tomographic content analysis : an application to Parkinson's disease (2017) 0.01 
    0.012991659 = product of:
  0.038974978 = sum of:
    0.038974978 = product of:
      0.11692493 = sum of:
        0.11692493 = weight(_text_:network in 3606) [ClassicSimilarity], result of:
          0.11692493 = score(doc=3606,freq=12.0), product of:
            0.19402927 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.043569047 = queryNorm
            0.6026149 = fieldWeight in 3606, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3606)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)
    Abstract
    To understand the current state of a discipline and to discover new knowledge of a certain theme, one builds bibliometric content networks based on the present knowledge entities. However, such networks can vary according to the collection of data sets relevant to the theme by querying knowledge entities. In this study we classify three different bibliometric content networks. The primary bibliometric network is based on knowledge entities relevant to a keyword of the theme, the secondary network is based on entities associated with the lower concepts of the keyword, and the tertiary network is based on entities influenced by the theme. To explore the content and properties of these networks, we propose a tomographic content analysis that takes a slice-and-dice approach to analyzing the networks. Our findings indicate that the primary network is best suited to understanding the current knowledge on a certain topic, whereas the secondary network is good at discovering new knowledge across fields associated with the topic, and the tertiary network is appropriate for outlining the current knowledge of the topic and relevant studies.
  2. Kim, M.; Baek, I.; Song, M.: Topic diffusion analysis of a weighted citation network in biomedical literature (2018) 0.01 
    0.0127291735 = product of:
  0.03818752 = sum of:
    0.03818752 = product of:
      0.114562556 = sum of:
        0.114562556 = weight(_text_:network in 4036) [ClassicSimilarity], result of:
          0.114562556 = score(doc=4036,freq=8.0), product of:
            0.19402927 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.043569047 = queryNorm
            0.59043956 = fieldWeight in 4036, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.046875 = fieldNorm(doc=4036)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)
    Abstract
    In this study, we propose a framework for detecting topic evolutions in weighted citation networks. Citation networks are important in studying knowledge flows; however, citation network analysis has primarily focused on binary networks in which the individual citation influences of each cited paper in a citing paper are considered identical, even though not all cited papers have a significant influence on the cited publication. Accordingly, it is necessary to build and analyze a citation network comprising scholarly publications that notably impact one another, thus identifying topic evolution in a more precise manner. To measure the strength of citation influence and identify paper topics, we employ a citation influence topic model primarily based on topical inheritance between cited and citing papers. Using scholarly publications in the field of the protein p53 as a case study, we build a citation network, filter it using citation influence values, and examine the diffusion of topics not only in the field but also in the subfields of p53.
  3. Song, M.; Kim, S.Y.; Zhang, G.; Ding, Y.; Chambers, T.: Productivity and influence in bioinformatics : a bibliometric analysis using PubMed central (2014) 0.00 
    0.003971059 = product of:
  0.011913176 = sum of:
    0.011913176 = product of:
      0.035739526 = sum of:
        0.035739526 = weight(_text_:29 in 1202) [ClassicSimilarity], result of:
          0.035739526 = score(doc=1202,freq=2.0), product of:
            0.15326229 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.043569047 = queryNorm
            0.23319192 = fieldWeight in 1202, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.046875 = fieldNorm(doc=1202)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)
    Date
    29. 1.2014 16:40:41
  4. Liu, M.; Bu, Y.; Chen, C.; Xu, J.; Li, D.; Leng, Y.; Freeman, R.B.; Meyer, E.T.; Yoon, W.; Sung, M.; Jeong, M.; Lee, J.; Kang, J.; Min, C.; Zhai, Y.; Song, M.; Ding, Y.: Pandemics are catalysts of scientific novelty : evidence from COVID-19 (2022) 0.00 
    0.003309216 = product of:
  0.009927647 = sum of:
    0.009927647 = product of:
      0.029782942 = sum of:
        0.029782942 = weight(_text_:29 in 633) [ClassicSimilarity], result of:
          0.029782942 = score(doc=633,freq=2.0), product of:
            0.15326229 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.043569047 = queryNorm
            0.19432661 = fieldWeight in 633, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.0390625 = fieldNorm(doc=633)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)
    Abstract
    Scientific novelty drives the efforts to invent new vaccines and solutions during the pandemic. First-time collaboration and international collaboration are two pivotal channels to expand teams' search activities for a broader scope of resources required to address the global challenge, which might facilitate the generation of novel ideas. Our analysis of 98,981 coronavirus papers suggests that scientific novelty measured by the BioBERT model that is pretrained on 29 million PubMed articles, and first-time collaboration increased after the outbreak of COVID-19, and international collaboration witnessed a sudden decrease. During COVID-19, papers with more first-time collaboration were found to be more novel and international collaboration did not hamper novelty as it had done in the normal periods. The findings suggest the necessity of reaching out for distant resources and the importance of maintaining a collaborative scientific community beyond nationalism during a pandemic.
  5. Song, M.; Kang, K.; An, J.Y.: Investigating drug-disease interactions in drug-symptom-disease triples via citation relations (2018) 0.00 
    0.003279447 = product of:
  0.009838341 = sum of:
    0.009838341 = product of:
      0.029515022 = sum of:
        0.029515022 = weight(_text_:22 in 4545) [ClassicSimilarity], result of:
          0.029515022 = score(doc=4545,freq=2.0), product of:
            0.15257138 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.043569047 = queryNorm
            0.19345059 = fieldWeight in 4545, 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=4545)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)
    Date
    1.11.2018 18:19:22

Authors

Years