Search (21 results, page 1 of 2)

  • × author_ss:"Liu, X."
  1. Chen, S.Y.; Liu, X.: ¬The contribution of data mining to information science : making sense of it all (2005) 0.02
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    Source
    Journal of information science. 30(2005) no.6, S.550-
  2. Liu, X.; Bu, Y.; Li, M.; Li, J.: Monodisciplinary collaboration disrupts science more than multidisciplinary collaboration (2024) 0.01
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    Abstract
    Collaboration across disciplines is a critical form of scientific collaboration to solve complex problems and make innovative contributions. This study focuses on the association between multidisciplinary collaboration measured by coauthorship in publications and the disruption of publications measured by the Disruption (D) index. We used authors' affiliations as a proxy of the disciplines to which they belong and categorized an article into multidisciplinary collaboration or monodisciplinary collaboration. The D index quantifies the extent to which a study disrupts its predecessors. We selected 13 journals that publish articles in six disciplines from the Microsoft Academic Graph (MAG) database and then constructed regression models with fixed effects and estimated the relationship between the variables. The findings show that articles with monodisciplinary collaboration are more disruptive than those with multidisciplinary collaboration. Furthermore, we uncovered the mechanism of how monodisciplinary collaboration disrupts science more than multidisciplinary collaboration by exploring the references of the sampled publications.
    Source
    Journal of the Association for Information Science and Technology. 75(2023) no.1, S.59-78
  3. Liu, X.; Yu, S.; Janssens, F.; Glänzel, W.; Moreau, Y.; Moor, B.de: Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database (2010) 0.01
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    Abstract
    We propose a new hybrid clustering framework to incorporate text mining with bibliometrics in journal set analysis. The framework integrates two different approaches: clustering ensemble and kernel-fusion clustering. To improve the flexibility and the efficiency of processing large-scale data, we propose an information-based weighting scheme to leverage the effect of multiple data sources in hybrid clustering. Three different algorithms are extended by the proposed weighting scheme and they are employed on a large journal set retrieved from the Web of Science (WoS) database. The clustering performance of the proposed algorithms is systematically evaluated using multiple evaluation methods, and they were cross-compared with alternative methods. Experimental results demonstrate that the proposed weighted hybrid clustering strategy is superior to other methods in clustering performance and efficiency. The proposed approach also provides a more refined structural mapping of journal sets, which is useful for monitoring and detecting new trends in different scientific fields.
    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.6, S.1105-1119
  4. Liu, X.; Chen, X.: Authors' noninstitutional emails and their correlation with retraction (2021) 0.01
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    Source
    Journal of the Association for Information Science and Technology. 72(2021) no.4, S.449-4473-477
  5. Liu, X.; Kaza, S.; Zhang, P.; Chen, H.: Determining inventor status and its effect on knowledge diffusion : a study on nanotechnology literature from China, Russia, and India (2011) 0.01
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    Abstract
    In an increasingly global research landscape, it is important to identify the most prolific researchers in various institutions and their influence on the diffusion of knowledge. Knowledge diffusion within institutions is influenced by not just the status of individual researchers but also the collaborative culture that determines status. There are various methods to measure individual status, but few studies have compared them or explored the possible effects of different cultures on the status measures. In this article, we examine knowledge diffusion within science and technology-oriented research organizations. Using social network analysis metrics to measure individual status in large-scale coauthorship networks, we studied an individual's impact on the recombination of knowledge to produce innovation in nanotechnology. Data from the most productive and high-impact institutions in China (Chinese Academy of Sciences), Russia (Russian Academy of Sciences), and India (Indian Institutes of Technology) were used. We found that boundary-spanning individuals influenced knowledge diffusion in all countries. However, our results also indicate that cultural and institutional differences may influence knowledge diffusion.
    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.6, S.1166-1176
  6. Liu, X.: ¬The standardization of Chinese library classification (1993) 0.01
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    Abstract
    The standardization of Chinese materials classification was first proposed in the late-1970s in China. In December 1980, the CCDST, the Chinese Library Association and the Chinese Society for Information Science proposed that the Chinese Library Classification system be adopted as national standard. This marked the beginning of the standardization of Chinese materials classification. Later on, there were many conferences and workshops held and four draft national standards were discussed, those for the Chinese Library Classification systems, the Materials Classification System, the Rules for Thesaurus and Subject Headings, and the rules for Materials Classifying Color Recognition. This article gives a brief review on the historical development of the standardization on Chinese Library Classification. It also discusses its effects on automation, networking and resources sharing and the feasibility of adopting Chinese Library Classification as a National Standard. In addition, the main content of the standardization of materials classification, use of the national standard classification system and variations under the standard system are covered in this article
  7. Frias-Martinez, E.; Chen, S.Y.; Liu, X.: Automatic cognitive style identification of digital library users for personalization (2007) 0.01
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    Source
    Journal of the American Society for Information Science and Technology. 58(2007) no.2, S.237-251
  8. Liu, X.: Generating metadata for cyberlearning resources through information retrieval and meta-search (2013) 0.01
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    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.4, S.771-786
  9. Liu, X.; Jia, H.: Answering academic questions for education by recommending cyberlearning resources (2013) 0.01
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    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.8, S.1707-1722
  10. Liu, X.; Turtle, H.: Real-time user interest modeling for real-time ranking (2013) 0.01
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    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.8, S.1557-1576
  11. Liu, X.; Guo, C.; Zhang, L.: Scholar metadata and knowledge generation with human and artificial intelligence (2014) 0.01
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    Source
    Journal of the Association for Information Science and Technology. 65(2014) no.6, S.1187-1201
  12. Zhang, X.; Fang, Y.; He, W.; Zhang, Y.; Liu, X.: Epistemic motivation, task reflexivity, and knowledge contribution behavior on team wikis : a cross-level moderation model (2019) 0.01
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    Source
    Journal of the Association for Information Science and Technology. 70(2019) no.5, S.448-461
  13. Liu, X.; Croft, W.B.: Statistical language modeling for information retrieval (2004) 0.00
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    Source
    Annual review of information science and technology. 39(2005), S.3-32
  14. Zhang, C.; Liu, X.; Xu, Y.(C.); Wang, Y.: Quality-structure index : a new metric to measure scientific journal influence (2011) 0.00
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    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.4, S.643-653
  15. Liu, X.; Zhang, J.; Guo, C.: Full-text citation analysis : a new method to enhance scholarly networks (2013) 0.00
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    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.9, S.1852-1863
  16. Liu, X.; Qin, J.: ¬An interactive metadata model for structural, descriptive, and referential representation of scholarly output (2014) 0.00
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    Source
    Journal of the Association for Information Science and Technology. 65(2014) no.5, S.964-983
  17. Chen, Z.; Huang, Y.; Tian, J.; Liu, X.; Fu, K.; Huang, T.: Joint model for subsentence-level sentiment analysis with Markov logic (2015) 0.00
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    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.9, S.1913-1922
  18. Jiang, Z.; Liu, X.; Chen, Y.: Recovering uncaptured citations in a scholarly network : a two-step citation analysis to estimate publication importance (2016) 0.00
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    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.7, S.1722-1735
  19. Liu, X.; Hu, M.; Xiao, B.S.; Shao, J.: Is my doctor around me? : Investigating the impact of doctors' presence on patients' review behaviors on an online health platform (2022) 0.00
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    Source
    Journal of the Association for Information Science and Technology. 73(2022) no.9, S.1279-1296
  20. Cui, Y.; Wang, Y.; Liu, X.; Wang, X.; Zhang, X.: Multidimensional scholarly citations : characterizing and understanding scholars' citation behaviors (2023) 0.00
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    Source
    Journal of the Association for Information Science and Technology. 74(2023) no.1, S.115-127