Using hybrid algorithmic-crowdsourcing methods for academic knowledge acquisition

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dc.contributor University of Helsinki, Department of Computer Science en
dc.contributor.author Dong, Zhaoan
dc.contributor.author Lu, Jiaheng
dc.contributor.author Ling, Tok Wang
dc.contributor.author Fan, Ju
dc.contributor.author Chen, Yueguo
dc.date.accessioned 2019-01-23T22:37:49Z
dc.date.available 2021-08-17T02:45:42Z
dc.date.issued 2017-12
dc.identifier.citation Dong , Z , Lu , J , Ling , T W , Fan , J & Chen , Y 2017 , ' Using hybrid algorithmic-crowdsourcing methods for academic knowledge acquisition ' , Cluster Computing , vol. 20 , no. 4 , pp. 3629-3641 . https://doi.org/10.1007/s10586-017-1089-8 en
dc.identifier.issn 1386-7857
dc.identifier.other PURE: 91386380
dc.identifier.other PURE UUID: c778d4f8-ebbf-49ea-8fd6-8afdd4869075
dc.identifier.other Scopus: 85029788772
dc.identifier.other WOS: 000414780400066
dc.identifier.other ORCID: /0000-0003-2067-454X/work/39925806
dc.identifier.uri http://hdl.handle.net/10138/298054
dc.description.abstract such as Figures, Tables, Definitions, Algo- rithms, etc., which are called Knowledge Cells hereafter. An advanced academic search engine which could take advantage of Knowledge Cells and their various relation- ships to obtain more accurate search results is expected. Further, it’s expected to provide a fine-grained search regard- ing to Knowledge Cells for deep-level information discovery and exploration. Therefore, it is important to identify and extract the Knowledge Cells and their various relationships which are often intrinsic and implicit in articles. With the exponential growth of scientific publications, discovery and acquisition of such useful academic knowledge impose some practical challenges For example, existing algorithmic meth- ods can hardly extend to handle diverse layouts of journals, nor to scale up to process massive documents. As crowd- sourcing has become a powerful paradigm for large scale problem-solving especially for tasks that are difficult for computers but easy for human, we consider the problem of academic knowledge discovery and acquisition as a crowd- sourced database problem and show a hybrid framework to integrate the accuracy of crowdsourcing workers and the speed of automatic algorithms. In this paper, we introduce our current system implementation, a platform for academic knowledge discovery and acquisition (PANDA), as well as some interesting observations and promising future directions. en
dc.language.iso eng
dc.relation.ispartof Cluster Computing
dc.rights en
dc.subject 113 Computer and information sciences en
dc.title Using hybrid algorithmic-crowdsourcing methods for academic knowledge acquisition en
dc.type Article
dc.description.version Peer reviewed
dc.identifier.doi https://doi.org/10.1007/s10586-017-1089-8
dc.type.uri info:eu-repo/semantics/other
dc.type.uri info:eu-repo/semantics/acceptedVersion
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