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  • × author_ss:"Feng, L."
  1. Kong, S.; Ye, F.; Feng, L.; Zhao, Z.: Towards the prediction problems of bursting hashtags on Twitter (2015) 0.00 
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    Abstract
    Hundreds of thousands of hashtags are generated every day on Twitter. Only a few will burst and become trending topics. In this article, we provide the definition of a bursting hashtag and conduct a systematic study of a series of challenging prediction problems that span the entire life cycles of bursting hashtags. Around the problem of "how to build a system to predict bursting hashtags," we explore different types of features and present machine learning solutions. On real data sets from Twitter, experiments are conducted to evaluate the effectiveness of the proposed solutions and the contributions of features.
    Type
    a
  2. Bartolo, L.M.; Lowe, C.S.; Melton, A.C.; Strahl, M.; Feng, L.; Woolverton, C.J.: Effectiveness of tagging laboratory data using Dublin Core in an electronic scientific notebook (2002) 0.00 
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    Abstract
    As a form of grey literature, scientific laboratory notebooks are intended to meet two broad functions: to record daily in-house activities as well as to manage research results. A major goal of this scientific electronic notebook project is to provide high quality resource discovery and retrieval capabilities for primary data objects produced in a multidisciplinary, biotechnology research laboratory study. This paper discusses a prototype modified relational database that incorporates Dublin Core metadata to organize and describe the laboratory data early in the scientific process. The study investigates the effectiveness of this approach to Support daily in-house tasks as well as to capture, integrate, and exchange research results.
    Source
    Gaining insight from research information (CRIS2002): Proceedings of the 6th International Conference an Current Research Information Systems, University of Kassel, August 29 - 31, 2002. Eds: W. Adamczak u. A. Nase
    Type
    a
  3. Feng, L.; Jeusfeld, M.A.; Hoppenbrouwers, J.: Beyond information searching and browsing : acquiring knowledge from digital libraries (2005) 0.00 
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    Abstract
    Digital libraries (DLs) are a resource for answering complex questions. Up to now, such systems mainly support keyword-based searching and browsing. The mapping from a research question to keywords and the assessment whether an article is relevant for a research question is completely with the user. In this paper, we present a two-layered digital library model. The aim is to enhance current DLs to support different levels of human cognitive acts, thus enabling new kinds of knowledge exchange among library users. The low layer of the model, namely, the tactical cognition support layer, provides users with requested relevant documents, as searching and browsing do. The upper layer of the model, namely, the strategic cognition support layer, not only provides users with relevant documents but also directly and intelligently answers users' cognitive questions. On the basis of the proposed model, we divide the DL information space into two subspaces, i.e., a knowledge subspace and a document subspace, where documents in the document subspace serves as the justification for the corresponding knowledge in the knowledge subspace. Detailed description of the knowledge subspace and its construction, as well as query facilities against the enhanced DLs for users' knowledge sharing and exchange, are particularly discussed.
    Type
    a

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