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  • × author_ss:"Zhang, L."
  • × year_i:[2010 TO 2020}
  1. Zhang, L.; Thijs, B.; Glänzel, W.: What does scientometrics share with other "metrics" sciences? (2013) 0.01 
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    Abstract
    In this article, the authors answer the question of whether the field of scientometrics/bibliometrics shares essential characteristics of "metrics" sciences. To achieve this objective, the citation network of seven selected metrics and their information environment is analyzed.
  2. Zhang, L.: Grasping the structure of journal articles : utilizing the functions of information units (2012) 0.01 
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    Date
    6. 4.2012 18:43:22
  3. Lee, H.-L.; Zhang, L.: Tracing the conceptions and treatment of genre in Anglo-American cataloging (2013) 0.01 
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    Abstract
    This study examines the conceptions and treatment of genre in four sets of modern Anglo-American cataloging rules spanning 171 years. Genre-related rules are first identified through "genre(s)," "form(s)," and "type(s)" keyword searches, and manual examination of the contents, then analyzed by level of treatment genre receives and by user tasks, as defined in the Functional Requirements for Bibliographic Records. While genre is found to be sporadically addressed across the rules, its significance has increased over time. In conclusion, the authors call for a rigorous and functional definition of genre and an integrated approach to genre in cataloging.
  4. Liu, X.; Guo, C.; Zhang, L.: Scholar metadata and knowledge generation with human and artificial intelligence (2014) 0.01 
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    Abstract
    Scholar metadata have traditionally centered on descriptive representations, which have been used as a foundation for scholarly publication repositories and academic information retrieval systems. In this article, we propose innovative and economic methods of generating knowledge-based structural metadata (structural keywords) using a combination of natural language processing-based machine-learning techniques and human intelligence. By allowing low-barrier participation through a social media system, scholars (both as authors and users) can participate in the metadata editing and enhancing process and benefit from more accurate and effective information retrieval. Our experimental web system ScholarWiki uses machine learning techniques, which automatically produce increasingly refined metadata by learning from the structural metadata contributed by scholars. The cumulated structural metadata add intelligence and automatically enhance and update recursively the quality of metadata, wiki pages, and the machine-learning model.

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