Search (945 results, page 1 of 48)

  • × year_i:[2010 TO 2020}
  1. Zeng, Q.; Yu, M.; Yu, W.; Xiong, J.; Shi, Y.; Jiang, M.: Faceted hierarchy : a new graph type to organize scientific concepts and a construction method (2019) 0.09
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    Content
    Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.
  2. HaCohen-Kerner, Y. et al.: Classification using various machine learning methods and combinations of key-phrases and visual features (2016) 0.09
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    Date
    1. 2.2016 18:25:22
  3. Zhou, Y. et al.: Analysing entity context in multilingual Wikipedia to support entity-centric retrieval applications (2016) 0.09
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    Date
    1. 2.2016 18:25:22
  4. Ding, Y.: Applying weighted PageRank to author citation networks (2011) 0.06
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    Date
    22. 1.2011 13:02:21
  5. Verwer, K.: Freiheit und Verantwortung bei Hans Jonas (2011) 0.05
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    Content
    Vgl.: http%3A%2F%2Fcreativechoice.org%2Fdoc%2FHansJonas.pdf&usg=AOvVaw1TM3teaYKgABL5H9yoIifA&opi=89978449.
  6. Zhang, Y.: Developing a holistic model for digital library evaluation (2010) 0.05
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    Abstract
    This article reports the author's recent research in developing a holistic model for various levels of digital library (DL) evaluation in which perceived important criteria from heterogeneous stakeholder groups are organized and presented. To develop such a model, the author applied a three-stage research approach: exploration, confirmation, and verification. During the exploration stage, a literature review was conducted followed by an interview, along with a card sorting technique, to collect important criteria perceived by DL experts. Then the criteria identified were used for developing an online survey during the confirmation stage. Survey respondents (431 in total) from 22 countries rated the importance of the criteria. A holistic DL evaluation model was constructed using statistical techniques. Eventually, the verification stage was devised to test the reliability of the model in the context of searching and evaluating an operational DL. The proposed model fills two lacunae in the DL domain: (a) the lack of a comprehensive and flexible framework to guide and benchmark evaluations, and (b) the uncertainty about what divergence exists among heterogeneous DL stakeholders, including general users.
  7. Küster, M.W.; Ludwig, C.; Al-Hajj, Y.; Aschenbrenner, A.: TextGrid: eScholarship und der Fortschritt der Wissenschaft durch vernetzte Angebote (2010) 0.05
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    Source
    Wissensspeicher in digitalen Räumen: Nachhaltigkeit - Verfügbarkeit - semantische Interoperabilität. Proceedings der 11. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation, Konstanz, 20. bis 22. Februar 2008. Hrsg.: J. Sieglerschmidt u. H.P.Ohly
  8. Ding, Y.; Zhang, G.; Chambers, T.; Song, M.; Wang, X.; Zhai, C.: Content-based citation analysis : the next generation of citation analysis (2014) 0.05
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    Date
    22. 8.2014 16:52:04
  9. Wang, Y.; Lee, J.-S.; Choi, I.-C.: Indexing by Latent Dirichlet Allocation and an Ensemble Model (2016) 0.05
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    Date
    12. 6.2016 21:39:22
  10. Crespo, J.A.; Herranz, N.; Li, Y.; Ruiz-Castillo, J.: ¬The effect on citation inequality of differences in citation practices at the web of science subject category level (2014) 0.05
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    Abstract
    This article studies the impact of differences in citation practices at the subfield, or Web of Science subject category level, using the model introduced in Crespo, Li, and Ruiz-Castillo (2013a), according to which the number of citations received by an article depends on its underlying scientific influence and the field to which it belongs. We use the same Thomson Reuters data set of about 4.4 million articles used in Crespo et al. (2013a) to analyze 22 broad fields. The main results are the following: First, when the classification system goes from 22 fields to 219 subfields the effect on citation inequality of differences in citation practices increases from ?14% at the field level to 18% at the subfield level. Second, we estimate a set of exchange rates (ERs) over a wide [660, 978] citation quantile interval to express the citation counts of articles into the equivalent counts in the all-sciences case. In the fractional case, for example, we find that in 187 of 219 subfields the ERs are reliable in the sense that the coefficient of variation is smaller than or equal to 0.10. Third, in the fractional case the normalization of the raw data using the ERs (or subfield mean citations) as normalization factors reduces the importance of the differences in citation practices from 18% to 3.8% (3.4%) of overall citation inequality. Fourth, the results in the fractional case are essentially replicated when we adopt a multiplicative approach.
  11. Kleineberg, M.: Context analysis and context indexing : formal pragmatics in knowledge organization (2014) 0.04
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    Source
    http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=5&ved=0CDQQFjAE&url=http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F3131107&ei=HzFWVYvGMsiNsgGTyoFI&usg=AFQjCNE2FHUeR9oQTQlNC4TPedv4Mo3DaQ&sig2=Rlzpr7a3BLZZkqZCXXN_IA&bvm=bv.93564037,d.bGg&cad=rja
  12. Chang, Y.-W.; Huang, M.-H.: ¬A study of the evolution of interdisciplinarity in library and information science : using three bibliometric methods (2012) 0.04
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    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.1, S.22-33
  13. Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015) 0.04
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    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22
  14. Choi, Y.; Syn, S.Y.: Characteristics of tagging behavior in digitized humanities online collections (2016) 0.04
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    Date
    21. 4.2016 11:23:22
  15. Wang, Y.; Shah, C.: Investigating failures in information seeking episodes (2017) 0.04
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  19. Qin, C.; Liu, Y.; Mou, J.; Chen, J.: User adoption of a hybrid social tagging approach in an online knowledge community (2019) 0.04
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  20. Gwizdka, J.; Moshfeghi, Y.; Wilson, M.L.: Introduction to the special issue on neuro-information science (2019) 0.03
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