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    Scientific Dataset Discovery via Topic-level Recommendation

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    Preprintfile1.pdf
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    Description:
    Pre-print
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    Type
    Preprint
    Authors
    Altaf, Basmah cc
    Pei, Shichao
    Zhang, Xiangliang cc
    KAUST Department
    Computer Science
    Computer Science Program
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Machine Intelligence & kNowledge Engineering Lab
    Date
    2021-06-07
    Permanent link to this record
    http://hdl.handle.net/10754/669474
    
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    Abstract
    Data intensive research requires the support of appropriate datasets. However, it is often time-consuming to discover usable datasets matching a specific research topic. We formulate the dataset discovery problem on an attributed heterogeneous graph, which is composed of paper-paper citation, paper-dataset citation, and also paper content. We propose to characterize both paper and dataset nodes by their commonly shared latent topics, rather than learning user and item representations via canonical graph embedding models, because the usage of datasets and the themes of research projects can be understood on the common base of research topics. The relevant datasets to a given research project can then be inferred in the shared topic space. The experimental results show that our model can generate reasonable profiles for datasets, and recommend proper datasets for a query, which represents a research project linked with several papers.
    Publisher
    arXiv
    arXiv
    2106.03399
    Additional Links
    https://arxiv.org/pdf/2106.03399.pdf
    Collections
    Preprints; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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