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dc.contributor.authorAltaf, Basmah
dc.contributor.authorPei, Shichao
dc.contributor.authorZhang, Xiangliang
dc.date.accessioned2021-06-09T06:43:29Z
dc.date.available2021-06-09T06:43:29Z
dc.date.issued2021-06-07
dc.identifier.urihttp://hdl.handle.net/10754/669474
dc.description.abstractData 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.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2106.03399.pdf
dc.rightsArchived with thanks to arXiv
dc.titleScientific Dataset Discovery via Topic-level Recommendation
dc.typePreprint
dc.contributor.departmentComputer Science
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.contributor.departmentMachine Intelligence & kNowledge Engineering Lab
dc.eprint.versionPre-print
dc.identifier.arxivid2106.03399
kaust.personAltaf, Basmah
kaust.personPei, Shichao
kaust.personZhang, Xiangliang
refterms.dateFOA2021-06-09T06:44:04Z


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