Search (45 results, page 1 of 3)

  • × author_ss:"Ding, Y."
  1. Ding, Y.: Applying weighted PageRank to author citation networks (2011) 0.07 
    0.070798025 = product of:
  0.10619704 = sum of:
    0.017095273 = weight(_text_:of in 4188) [ClassicSimilarity], result of:
      0.017095273 = score(doc=4188,freq=6.0), product of:
        0.08160993 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.05218836 = queryNorm
        0.20947541 = fieldWeight in 4188, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4188)
    0.08910176 = sum of:
      0.039606147 = weight(_text_:science in 4188) [ClassicSimilarity], result of:
        0.039606147 = score(doc=4188,freq=4.0), product of:
          0.13747036 = queryWeight, product of:
            2.6341193 = idf(docFreq=8627, maxDocs=44218)
            0.05218836 = queryNorm
          0.2881068 = fieldWeight in 4188, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            2.6341193 = idf(docFreq=8627, maxDocs=44218)
            0.0546875 = fieldNorm(doc=4188)
      0.049495615 = weight(_text_:22 in 4188) [ClassicSimilarity], result of:
        0.049495615 = score(doc=4188,freq=2.0), product of:
          0.18275474 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05218836 = queryNorm
          0.2708308 = fieldWeight in 4188, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0546875 = fieldNorm(doc=4188)
  0.6666667 = coord(2/3)
    Abstract
    This article aims to identify whether different weighted PageRank algorithms can be applied to author citation networks to measure the popularity and prestige of a scholar from a citation perspective. Information retrieval (IR) was selected as a test field and data from 1956-2008 were collected from Web of Science. Weighted PageRank with citation and publication as weighted vectors were calculated on author citation networks. The results indicate that both popularity rank and prestige rank were highly correlated with the weighted PageRank. Principal component analysis was conducted to detect relationships among these different measures. For capturing prize winners within the IR field, prestige rank outperformed all the other measures
    Date
    22. 1.2011 13:02:21
    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.2, S.236-245
  2. Ding, Y.; Zhang, G.; Chambers, T.; Song, M.; Wang, X.; Zhai, C.: Content-based citation analysis : the next generation of citation analysis (2014) 0.06 
    0.06120644 = product of:
  0.09180966 = sum of:
    0.025379896 = weight(_text_:of in 1521) [ClassicSimilarity], result of:
      0.025379896 = score(doc=1521,freq=18.0), product of:
        0.08160993 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.05218836 = queryNorm
        0.3109903 = fieldWeight in 1521, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=1521)
    0.066429764 = sum of:
      0.02400495 = weight(_text_:science in 1521) [ClassicSimilarity], result of:
        0.02400495 = score(doc=1521,freq=2.0), product of:
          0.13747036 = queryWeight, product of:
            2.6341193 = idf(docFreq=8627, maxDocs=44218)
            0.05218836 = queryNorm
          0.17461908 = fieldWeight in 1521, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            2.6341193 = idf(docFreq=8627, maxDocs=44218)
            0.046875 = fieldNorm(doc=1521)
      0.042424813 = weight(_text_:22 in 1521) [ClassicSimilarity], result of:
        0.042424813 = score(doc=1521,freq=2.0), product of:
          0.18275474 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05218836 = queryNorm
          0.23214069 = fieldWeight in 1521, 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=1521)
  0.6666667 = coord(2/3)
    Abstract
    Traditional citation analysis has been widely applied to detect patterns of scientific collaboration, map the landscapes of scholarly disciplines, assess the impact of research outputs, and observe knowledge transfer across domains. It is, however, limited, as it assumes all citations are of similar value and weights each equally. Content-based citation analysis (CCA) addresses a citation's value by interpreting each one based on its context at both the syntactic and semantic levels. This paper provides a comprehensive overview of CAA research in terms of its theoretical foundations, methodical approaches, and example applications. In addition, we highlight how increased computational capabilities and publicly available full-text resources have opened this area of research to vast possibilities, which enable deeper citation analysis, more accurate citation prediction, and increased knowledge discovery.
    Date
    22. 8.2014 16:52:04
    Source
    Journal of the Association for Information Science and Technology. 65(2014) no.9, S.1820-1833
  3. Ding, Y.; Foo, S.: Ontology research and development : part 1 - a review of ontology generation (2002) 0.04 
    0.037281495 = product of:
  0.05592224 = sum of:
    0.027916465 = weight(_text_:of in 3808) [ClassicSimilarity], result of:
      0.027916465 = score(doc=3808,freq=4.0), product of:
        0.08160993 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.05218836 = queryNorm
        0.34207192 = fieldWeight in 3808, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.109375 = fieldNorm(doc=3808)
    0.028005775 = product of:
      0.05601155 = sum of:
        0.05601155 = weight(_text_:science in 3808) [ClassicSimilarity], result of:
          0.05601155 = score(doc=3808,freq=2.0), product of:
            0.13747036 = queryWeight, product of:
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.05218836 = queryNorm
            0.40744454 = fieldWeight in 3808, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.109375 = fieldNorm(doc=3808)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Source
    Journal of information science. 28(2002) no.2, S.123-136
  4. Ding, Y.: ¬A review of ontologies with the Semantic Web in view (2001) 0.04 
    0.037281495 = product of:
  0.05592224 = sum of:
    0.027916465 = weight(_text_:of in 4152) [ClassicSimilarity], result of:
      0.027916465 = score(doc=4152,freq=4.0), product of:
        0.08160993 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.05218836 = queryNorm
        0.34207192 = fieldWeight in 4152, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.109375 = fieldNorm(doc=4152)
    0.028005775 = product of:
      0.05601155 = sum of:
        0.05601155 = weight(_text_:science in 4152) [ClassicSimilarity], result of:
          0.05601155 = score(doc=4152,freq=2.0), product of:
            0.13747036 = queryWeight, product of:
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.05218836 = queryNorm
            0.40744454 = fieldWeight in 4152, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.109375 = fieldNorm(doc=4152)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Source
    Journal of information science. 27(2001) no.?, S.377-384
  5. Xu, H.; Bu, Y.; Liu, M.; Zhang, C.; Sun, M.; Zhang, Y.; Meyer, E.; Salas, E.; Ding, Y.: Team power dynamics and team impact : new perspectives on scientific collaboration using career age as a proxy for team power (2022) 0.04 
    0.03584493 = product of:
  0.053767394 = sum of:
    0.027304424 = weight(_text_:of in 663) [ClassicSimilarity], result of:
      0.027304424 = score(doc=663,freq=30.0), product of:
        0.08160993 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.05218836 = queryNorm
        0.33457235 = fieldWeight in 663, product of:
          5.477226 = tf(freq=30.0), with freq of:
            30.0 = termFreq=30.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=663)
    0.02646297 = product of:
      0.05292594 = sum of:
        0.05292594 = weight(_text_:science in 663) [ClassicSimilarity], result of:
          0.05292594 = score(doc=663,freq=14.0), product of:
            0.13747036 = queryWeight, product of:
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.05218836 = queryNorm
            0.38499892 = fieldWeight in 663, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.0390625 = fieldNorm(doc=663)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    Power dynamics influence every aspect of scientific collaboration. Team power dynamics can be measured by team power level and team power hierarchy. Team power level is conceptualized as the average level of the possession of resources, expertise, or decision-making authorities of a team. Team power hierarchy represents the vertical differences of the possessions of resources in a team. In Science of Science, few studies have looked at scientific collaboration from the perspective of team power dynamics. This research examines how team power dynamics affect team impact to fill the research gap. In this research, all coauthors of one publication are treated as one team. Team power level and team power hierarchy of one team are measured by the mean and Gini index of career age of coauthors in this team. Team impact is quantified by citations of a paper authored by this team. By analyzing over 7.7 million teams from Science (e.g., Computer Science, Physics), Social Sciences (e.g., Sociology, Library & Information Science), and Arts & Humanities (e.g., Art), we find that flat team structure is associated with higher team impact, especially when teams have high team power level. These findings have been repeated in all five disciplines except Art, and are consistent in various types of teams from Computer Science including teams from industry or academia, teams with different gender groups, teams with geographical contrast, and teams with distinct size.
    Source
    Journal of the Association for Information Science and Technology. 73(2022) no.10, S.1489-1505
  6. Ni, C.; Shaw, D.; Lind, S.M.; Ding, Y.: Journal impact and proximity : an assessment using bibliographic features (2013) 0.03 
    0.034194496 = product of:
  0.05129174 = sum of:
    0.03050284 = weight(_text_:of in 686) [ClassicSimilarity], result of:
      0.03050284 = score(doc=686,freq=26.0), product of:
        0.08160993 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.05218836 = queryNorm
        0.37376386 = fieldWeight in 686, product of:
          5.0990195 = tf(freq=26.0), with freq of:
            26.0 = termFreq=26.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=686)
    0.020788899 = product of:
      0.041577797 = sum of:
        0.041577797 = weight(_text_:science in 686) [ClassicSimilarity], result of:
          0.041577797 = score(doc=686,freq=6.0), product of:
            0.13747036 = queryWeight, product of:
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.05218836 = queryNorm
            0.30244917 = fieldWeight in 686, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.046875 = fieldNorm(doc=686)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    Journals in the Information Science & Library Science category of Journal Citation Reports (JCR) were compared using both bibliometric and bibliographic features. Data collected covered journal impact factor (JIF), number of issues per year, number of authors per article, longevity, editorial board membership, frequency of publication, number of databases indexing the journal, number of aggregators providing full-text access, country of publication, JCR categories, Dewey decimal classification, and journal statement of scope. Three features significantly correlated with JIF: number of editorial board members and number of JCR categories in which a journal is listed correlated positively; journal longevity correlated negatively with JIF. Coword analysis of journal descriptions provided a proximity clustering of journals, which differed considerably from the clusters based on editorial board membership. Finally, a multiple linear regression model was built to predict the JIF based on all the collected bibliographic features.
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.4, S.802-817
  7. Milojevic, S.; Sugimoto, C.R.; Yan, E.; Ding, Y.: ¬The cognitive structure of Library and Information Science : analysis of article title words (2011) 0.03 
    0.033276416 = product of:
  0.04991462 = sum of:
    0.029910497 = weight(_text_:of in 4608) [ClassicSimilarity], result of:
      0.029910497 = score(doc=4608,freq=36.0), product of:
        0.08160993 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.05218836 = queryNorm
        0.36650562 = fieldWeight in 4608, product of:
          6.0 = tf(freq=36.0), with freq of:
            36.0 = termFreq=36.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4608)
    0.020004123 = product of:
      0.040008247 = sum of:
        0.040008247 = weight(_text_:science in 4608) [ClassicSimilarity], result of:
          0.040008247 = score(doc=4608,freq=8.0), product of:
            0.13747036 = queryWeight, product of:
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.05218836 = queryNorm
            0.2910318 = fieldWeight in 4608, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4608)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    This study comprises a suite of analyses of words in article titles in order to reveal the cognitive structure of Library and Information Science (LIS). The use of title words to elucidate the cognitive structure of LIS has been relatively neglected. The present study addresses this gap by performing (a) co-word analysis and hierarchical clustering, (b) multidimensional scaling, and (c) determination of trends in usage of terms. The study is based on 10,344 articles published between 1988 and 2007 in 16 LIS journals. Methodologically, novel aspects of this study are: (a) its large scale, (b) removal of non-specific title words based on the "word concentration" measure (c) identification of the most frequent terms that include both single words and phrases, and (d) presentation of the relative frequencies of terms using "heatmaps". Conceptually, our analysis reveals that LIS consists of three main branches: the traditionally recognized library-related and information-related branches, plus an equally distinct bibliometrics/scientometrics branch. The three branches focus on: libraries, information, and science, respectively. In addition, our study identifies substructures within each branch. We also tentatively identify "information seeking behavior" as a branch that is establishing itself separate from the three main branches. Furthermore, we find that cognitive concepts in LIS evolve continuously, with no stasis since 1992. The most rapid development occurred between 1998 and 2001, influenced by the increased focus on the Internet. The change in the cognitive landscape is found to be driven by the emergence of new information technologies, and the retirement of old ones.
    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.10, S.1933-1953
  8. Ding, Y.; Chowdhury, G.C.; Foo, S.: Incorporating the results of co-word analyses to increase search variety for information retrieval (2000) 0.03 
    0.031955566 = product of:
  0.047933348 = sum of:
    0.023928396 = weight(_text_:of in 6328) [ClassicSimilarity], result of:
      0.023928396 = score(doc=6328,freq=4.0), product of:
        0.08160993 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.05218836 = queryNorm
        0.2932045 = fieldWeight in 6328, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.09375 = fieldNorm(doc=6328)
    0.02400495 = product of:
      0.0480099 = sum of:
        0.0480099 = weight(_text_:science in 6328) [ClassicSimilarity], result of:
          0.0480099 = score(doc=6328,freq=2.0), product of:
            0.13747036 = queryWeight, product of:
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.05218836 = queryNorm
            0.34923816 = fieldWeight in 6328, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.09375 = fieldNorm(doc=6328)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Source
    Journal of information science. 26(2000) no.6, S.429-451
  9. Ding, Y.; Foo, S.: Ontology research and development : part 2 - a review of ontology mapping and evolving (2002) 0.03 
    0.031955566 = product of:
  0.047933348 = sum of:
    0.023928396 = weight(_text_:of in 3835) [ClassicSimilarity], result of:
      0.023928396 = score(doc=3835,freq=4.0), product of:
        0.08160993 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.05218836 = queryNorm
        0.2932045 = fieldWeight in 3835, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.09375 = fieldNorm(doc=3835)
    0.02400495 = product of:
      0.0480099 = sum of:
        0.0480099 = weight(_text_:science in 3835) [ClassicSimilarity], result of:
          0.0480099 = score(doc=3835,freq=2.0), product of:
            0.13747036 = queryWeight, product of:
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.05218836 = queryNorm
            0.34923816 = fieldWeight in 3835, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.09375 = fieldNorm(doc=3835)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Source
    Journal of information science. 28(2002) no.3, S.375-388
  10. Yan, E.; Ding, Y.: Weighted citation : an indicator of an article's prestige (2010) 0.03 
    0.031903222 = product of:
  0.04785483 = sum of:
    0.025222747 = weight(_text_:of in 3705) [ClassicSimilarity], result of:
      0.025222747 = score(doc=3705,freq=10.0), product of:
        0.08160993 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.05218836 = queryNorm
        0.3090647 = fieldWeight in 3705, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0625 = fieldNorm(doc=3705)
    0.022632083 = product of:
      0.045264166 = sum of:
        0.045264166 = weight(_text_:science in 3705) [ClassicSimilarity], result of:
          0.045264166 = score(doc=3705,freq=4.0), product of:
            0.13747036 = queryWeight, product of:
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.05218836 = queryNorm
            0.3292649 = fieldWeight in 3705, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.0625 = fieldNorm(doc=3705)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    The authors propose using the technique of weighted citation to measure an article's prestige. The technique allocates a different weight to each reference by taking into account the impact of citing journals and citation time intervals. Weightedcitation captures prestige, whereas citation counts capture popularity. They compare the value variances for popularity and prestige for articles published in the Journal of the American Society for Information Science and Technology from 1998 to 2007, and find that the majority have comparable status.
    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.8, S.1635-1643
  11. Li, R.; Chambers, T.; Ding, Y.; Zhang, G.; Meng, L.: Patent citation analysis : calculating science linkage based on citing motivation (2014) 0.03 
    0.030935537 = product of:
  0.046403304 = sum of:
    0.019940332 = weight(_text_:of in 1257) [ClassicSimilarity], result of:
      0.019940332 = score(doc=1257,freq=16.0), product of:
        0.08160993 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.05218836 = queryNorm
        0.24433708 = fieldWeight in 1257, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1257)
    0.02646297 = product of:
      0.05292594 = sum of:
        0.05292594 = weight(_text_:science in 1257) [ClassicSimilarity], result of:
          0.05292594 = score(doc=1257,freq=14.0), product of:
            0.13747036 = queryWeight, product of:
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.05218836 = queryNorm
            0.38499892 = fieldWeight in 1257, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1257)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    Science linkage is a widely used patent bibliometric indicator to measure patent linkage to scientific research based on the frequency of citations to scientific papers within the patent. Science linkage is also regarded as noisy because the subject of patent citation behavior varies from inventors/applicants to examiners. In order to identify and ultimately reduce this noise, we analyzed the different citing motivations of examiners and inventors/applicants. We built 4 hypotheses based upon our study of patent law, the unique economic nature of a patent, and a patent citation's market effect. To test our hypotheses, we conducted an expert survey based on our science linkage calculation in the domain of catalyst from U.S. patent data (2006-2009) over 3 types of citations: self-citation by inventor/applicant, non-self-citation by inventor/applicant, and citation by examiner. According to our results, evaluated by domain experts, we conclude that the non-self-citation by inventor/applicant is quite noisy and cannot indicate science linkage and that self-citation by inventor/applicant, although limited, is more appropriate for understanding science linkage.
    Source
    Journal of the Association for Information Science and Technology. 65(2014) no.5, S.1007-1017
  12. Zhang, G.; Ding, Y.; Milojevic, S.: Citation content analysis (CCA) : a framework for syntactic and semantic analysis of citation content (2013) 0.03 
    0.029151216 = product of:
  0.043726824 = sum of:
    0.02675276 = weight(_text_:of in 975) [ClassicSimilarity], result of:
      0.02675276 = score(doc=975,freq=20.0), product of:
        0.08160993 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.05218836 = queryNorm
        0.32781258 = fieldWeight in 975, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=975)
    0.016974064 = product of:
      0.033948127 = sum of:
        0.033948127 = weight(_text_:science in 975) [ClassicSimilarity], result of:
          0.033948127 = score(doc=975,freq=4.0), product of:
            0.13747036 = queryWeight, product of:
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.05218836 = queryNorm
            0.24694869 = fieldWeight in 975, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.046875 = fieldNorm(doc=975)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    This study proposes a new framework for citation content analysis (CCA), for syntactic and semantic analysis of citation content that can be used to better analyze the rich sociocultural context of research behavior. This framework could be considered the next generation of citation analysis. The authors briefly review the history and features of content analysis in traditional social sciences and its previous application in library and information science (LIS). Based on critical discussion of the theoretical necessity of a new method as well as the limits of citation analysis, the nature and purposes of CCA are discussed, and potential procedures to conduct CCA, including principles to identify the reference scope, a two-dimensional (citing and cited) and two-module (syntactic and semantic) codebook, are provided and described. Future work and implications are also suggested.
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.7, S.1490-1503
  13. Ding, Y.; Jacob, E.K.; Fried, M.; Toma, I.; Yan, E.; Foo, S.; Milojevicacute, S.: Upper tag ontology for integrating social tagging data (2010) 0.03 
    0.028336879 = product of:
  0.042505316 = sum of:
    0.03050284 = weight(_text_:of in 3421) [ClassicSimilarity], result of:
      0.03050284 = score(doc=3421,freq=26.0), product of:
        0.08160993 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.05218836 = queryNorm
        0.37376386 = fieldWeight in 3421, product of:
          5.0990195 = tf(freq=26.0), with freq of:
            26.0 = termFreq=26.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=3421)
    0.012002475 = product of:
      0.02400495 = sum of:
        0.02400495 = weight(_text_:science in 3421) [ClassicSimilarity], result of:
          0.02400495 = score(doc=3421,freq=2.0), product of:
            0.13747036 = queryWeight, product of:
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.05218836 = queryNorm
            0.17461908 = fieldWeight in 3421, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.046875 = fieldNorm(doc=3421)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    Data integration and mediation have become central concerns of information technology over the past few decades. With the advent of the Web and the rapid increases in the amount of data and the number of Web documents and users, researchers have focused on enhancing the interoperability of data through the development of metadata schemes. Other researchers have looked to the wealth of metadata generated by bookmarking sites on the Social Web. While several existing ontologies have capitalized on the semantics of metadata created by tagging activities, the Upper Tag Ontology (UTO) emphasizes the structure of tagging activities to facilitate modeling of tagging data and the integration of data from different bookmarking sites as well as the alignment of tagging ontologies. UTO is described and its utility in modeling, harvesting, integrating, searching, and analyzing data is demonstrated with metadata harvested from three major social tagging systems (Delicious, Flickr, and YouTube).
    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.3, S.505-521
  14. Sugimoto, C.R.; Li, D.; Russell, T.G.; Finlay, S.C.; Ding, Y.: ¬The shifting sands of disciplinary development : analyzing North American Library and Information Science dissertations using latent Dirichlet allocation (2011) 0.03 
    0.028198726 = product of:
  0.04229809 = sum of:
    0.022293966 = weight(_text_:of in 4143) [ClassicSimilarity], result of:
      0.022293966 = score(doc=4143,freq=20.0), product of:
        0.08160993 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.05218836 = queryNorm
        0.27317715 = fieldWeight in 4143, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4143)
    0.020004123 = product of:
      0.040008247 = sum of:
        0.040008247 = weight(_text_:science in 4143) [ClassicSimilarity], result of:
          0.040008247 = score(doc=4143,freq=8.0), product of:
            0.13747036 = queryWeight, product of:
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.05218836 = queryNorm
            0.2910318 = fieldWeight in 4143, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4143)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    This work identifies changes in dominant topics in library and information science (LIS) over time, by analyzing the 3,121 doctoral dissertations completed between 1930 and 2009 at North American Library and Information Science programs. The authors utilize latent Dirichlet allocation (LDA) to identify latent topics diachronically and to identify representative dissertations of those topics. The findings indicate that the main topics in LIS have changed substantially from those in the initial period (1930-1969) to the present (2000-2009). However, some themes occurred in multiple periods, representing core areas of the field: library history occurred in the first two periods; citation analysis in the second and third periods; and information-seeking behavior in the fourth and last period. Two topics occurred in three of the five periods: information retrieval and information use. One of the notable changes in the topics was the diminishing use of the word library (and related terms). This has implications for the provision of doctoral education in LIS. This work is compared to other earlier analyses and provides validation for the use of LDA in topic analysis of a discipline.
    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.1, S.185-204
  15. Yan, E.; Ding, Y.: Applying centrality measures to impact analysis : a coauthorship network analysis (2009) 0.03 
    0.02791532 = product of:
  0.04187298 = sum of:
    0.022069903 = weight(_text_:of in 3083) [ClassicSimilarity], result of:
      0.022069903 = score(doc=3083,freq=10.0), product of:
        0.08160993 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.05218836 = queryNorm
        0.2704316 = fieldWeight in 3083, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=3083)
    0.019803073 = product of:
      0.039606147 = sum of:
        0.039606147 = weight(_text_:science in 3083) [ClassicSimilarity], result of:
          0.039606147 = score(doc=3083,freq=4.0), product of:
            0.13747036 = queryWeight, product of:
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.05218836 = queryNorm
            0.2881068 = fieldWeight in 3083, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3083)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    Many studies on coauthorship networks focus on network topology and network statistical mechanics. This article takes a different approach by studying micro-level network properties with the aim of applying centrality measures to impact analysis. Using coauthorship data from 16 journals in the field of library and information science (LIS) with a time span of 20 years (1988-2007), we construct an evolving coauthorship network and calculate four centrality measures (closeness centrality, betweenness centrality, degree centrality, and PageRank) for authors in this network. We find that the four centrality measures are significantly correlated with citation counts. We also discuss the usability of centrality measures in author ranking and suggest that centrality measures can be useful indicators for impact analysis.
    Source
    Journal of the American Society for Information Science and Technology. 60(2009) no.10, S.2107-2118
  16. Zhai, Y; Ding, Y.; Wang, F.: Measuring the diffusion of an innovation : a citation analysis (2018) 0.03 
    0.025836824 = product of:
  0.038755234 = sum of:
    0.02675276 = weight(_text_:of in 4116) [ClassicSimilarity], result of:
      0.02675276 = score(doc=4116,freq=20.0), product of:
        0.08160993 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.05218836 = queryNorm
        0.32781258 = fieldWeight in 4116, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=4116)
    0.012002475 = product of:
      0.02400495 = sum of:
        0.02400495 = weight(_text_:science in 4116) [ClassicSimilarity], result of:
          0.02400495 = score(doc=4116,freq=2.0), product of:
            0.13747036 = queryWeight, product of:
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.05218836 = queryNorm
            0.17461908 = fieldWeight in 4116, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.046875 = fieldNorm(doc=4116)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    Innovations transform our research traditions and become the driving force to advance individual, group, and social creativity. Meanwhile, interdisciplinary research is increasingly being promoted as a route to advance the complex challenges we face as a society. In this paper, we use Latent Dirichlet Allocation (LDA) citation as a proxy context for the diffusion of an innovation. With an analysis of topic evolution, we divide the diffusion process into five stages: testing and evaluation, implementation, improvement, extending, and fading. Through a correlation analysis of topic and subject, we show the application of LDA in different subjects. We also reveal the cross-boundary diffusion between different subjects based on the analysis of the interdisciplinary studies. The results show that as LDA is transferred into different areas, the adoption of each subject is relatively adjacent to those with similar research interests. Our findings further support researchers' understanding of the impact formation of innovation.
    Source
    Journal of the Association for Information Science and Technology. 69(2018) no.3, S.368-379
  17. Hu, B.; Dong, X.; Zhang, C.; Bowman, T.D.; Ding, Y.; Milojevic, S.; Ni, C.; Yan, E.; Larivière, V.: ¬A lead-lag analysis of the topic evolution patterns for preprints and publications (2015) 0.03 
    0.025131106 = product of:
  0.03769666 = sum of:
    0.020722598 = weight(_text_:of in 2337) [ClassicSimilarity], result of:
      0.020722598 = score(doc=2337,freq=12.0), product of:
        0.08160993 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.05218836 = queryNorm
        0.25392252 = fieldWeight in 2337, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=2337)
    0.016974064 = product of:
      0.033948127 = sum of:
        0.033948127 = weight(_text_:science in 2337) [ClassicSimilarity], result of:
          0.033948127 = score(doc=2337,freq=4.0), product of:
            0.13747036 = queryWeight, product of:
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.05218836 = queryNorm
            0.24694869 = fieldWeight in 2337, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.046875 = fieldNorm(doc=2337)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    This study applied LDA (latent Dirichlet allocation) and regression analysis to conduct a lead-lag analysis to identify different topic evolution patterns between preprints and papers from arXiv and the Web of Science (WoS) in astrophysics over the last 20 years (1992-2011). Fifty topics in arXiv and WoS were generated using an LDA algorithm and then regression models were used to explain 4 types of topic growth patterns. Based on the slopes of the fitted equation curves, the paper redefines the topic trends and popularity. Results show that arXiv and WoS share similar topics in a given domain, but differ in evolution trends. Topics in WoS lose their popularity much earlier and their durations of popularity are shorter than those in arXiv. This work demonstrates that open access preprints have stronger growth tendency as compared to traditional printed publications.
    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.12, S.2643-2656
  18. Huang, Y.; Bu, Y.; Ding, Y.; Lu, W.: From zero to one : a perspective on citing (2019) 0.03 
    0.025131106 = product of:
  0.03769666 = sum of:
    0.020722598 = weight(_text_:of in 5387) [ClassicSimilarity], result of:
      0.020722598 = score(doc=5387,freq=12.0), product of:
        0.08160993 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.05218836 = queryNorm
        0.25392252 = fieldWeight in 5387, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=5387)
    0.016974064 = product of:
      0.033948127 = sum of:
        0.033948127 = weight(_text_:science in 5387) [ClassicSimilarity], result of:
          0.033948127 = score(doc=5387,freq=4.0), product of:
            0.13747036 = queryWeight, product of:
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.05218836 = queryNorm
            0.24694869 = fieldWeight in 5387, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.046875 = fieldNorm(doc=5387)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    This article investigates the lengths of time that publications with different numbers of citations take to receive their first citation (the beginning stage), and then compares the lengths of time to receive two or more citations after receiving the first citation (the accumulative stage) in the field of computer science. We find that in the beginning stage, that is, from zero to one citation, high-, medium-, and low-cited publications do not obviously exhibit different lengths of time. However, in the accumulative stage, that is, from one to N citations, highly cited publications begin to receive citations much more rapidly than medium- and low-cited publications. Moreover, as N increases, the difference in receiving new citations among high-, medium-, and low-cited publications increases quite significantly.
    Source
    Journal of the Association for Information Science and Technology. 70(2019) no.10, S.1098-1107
  19. Zhai, Y.; Ding, Y.; Zhang, H.: Innovation adoption : broadcasting versus virality (2021) 0.02 
    0.024921581 = product of:
  0.03738237 = sum of:
    0.025379896 = weight(_text_:of in 162) [ClassicSimilarity], result of:
      0.025379896 = score(doc=162,freq=18.0), product of:
        0.08160993 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.05218836 = queryNorm
        0.3109903 = fieldWeight in 162, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=162)
    0.012002475 = product of:
      0.02400495 = sum of:
        0.02400495 = weight(_text_:science in 162) [ClassicSimilarity], result of:
          0.02400495 = score(doc=162,freq=2.0), product of:
            0.13747036 = queryWeight, product of:
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.05218836 = queryNorm
            0.17461908 = fieldWeight in 162, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.046875 = fieldNorm(doc=162)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    Diffusion channels are critical to determining the adoption scale, which leads to the ultimate impact of an innovation. The aim of this study is to develop an integrative understanding of the impact of two diffusion channels (i.e., broadcasting vs. virality) on innovation adoption. Using citations of a series of classic algorithms and the time series of co-authorship as the footprints of their diffusion trajectories, we propose a novel method to analyze the intertwining relationships between broadcasting and virality in the innovation diffusion process. Our findings show that broadcasting and virality have similar diffusion power, but play different roles across diffusion stages. Broadcasting is more powerful in the early stages but may be gradually caught up or even surpassed by virality in the later period. Meanwhile, diffusion speed in virality is significantly faster than broadcasting and members from virality channels tend to adopt the same innovation repetitively.
    Source
    Journal of the Association for Information Science and Technology. 72(2021) no.4, S.403-416
  20. He, B.; Ding, Y.; Ni, C.: Mining enriched contextual information of scientific collaboration : a meso perspective (2011) 0.02 
    0.024292678 = product of:
  0.036439016 = sum of:
    0.022293966 = weight(_text_:of in 4444) [ClassicSimilarity], result of:
      0.022293966 = score(doc=4444,freq=20.0), product of:
        0.08160993 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.05218836 = queryNorm
        0.27317715 = fieldWeight in 4444, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4444)
    0.014145052 = product of:
      0.028290104 = sum of:
        0.028290104 = weight(_text_:science in 4444) [ClassicSimilarity], result of:
          0.028290104 = score(doc=4444,freq=4.0), product of:
            0.13747036 = queryWeight, product of:
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.05218836 = queryNorm
            0.20579056 = fieldWeight in 4444, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.6341193 = idf(docFreq=8627, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4444)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    Studying scientific collaboration using coauthorship networks has attracted much attention in recent years. How and in what context two authors collaborate remain among the major questions. Previous studies, however, have focused on either exploring the global topology of coauthorship networks (macro perspective) or ranking the impact of individual authors (micro perspective). Neither of them has provided information on the context of the collaboration between two specific authors, which may potentially imply rich socioeconomic, disciplinary, and institutional information on collaboration. Different from the macro perspective and micro perspective, this article proposes a novel method (meso perspective) to analyze scientific collaboration, in which a contextual subgraph is extracted as the unit of analysis. A contextual subgraph is defined as a small subgraph of a large-scale coauthorship network that captures relationship and context between two coauthors. This method is applied to the field of library and information science. Topological properties of all the subgraphs in four time spans are investigated, including size, average degree, clustering coefficient, and network centralization. Results show that contextual subgprahs capture useful contextual information on two authors' collaboration.
    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.5, S.831-845

Authors

Years

Types

  • a 45
  • b 1
  • More… Less…

Themes