=Paper=
{{Paper
|id=Vol-2658/keynote2
|storemode=property
|title=Building Scholarly Knowledge Bases with Crowdsourcing and Text Mining
|pdfUrl=https://ceur-ws.org/Vol-2658/keynote2.pdf
|volume=Vol-2658
|authors=Markus Stocker
|dblpUrl=https://dblp.org/rec/conf/jcdl/Stocker20
}}
==Building Scholarly Knowledge Bases with Crowdsourcing and Text Mining==
<pdf width="1500px">https://ceur-ws.org/Vol-2658/keynote2.pdf</pdf>
<pre>
            EEKE 2020 - Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents




       Building Scholarly Knowledge Bases with Crowdsourcing
                                               and Text Mining

                                                    Markus Stocker

               TIB — Leibniz Information Centre for Science and Technology, Germany
                                      Markus.Stocker@tib.eu


    Abstract

    For centuries, scholarly knowledge has been buried in documents. While articles are
    great to convey the story of scientific work to peers, they make it hard for machines to
    process scholarly knowledge. The recent proliferation of the scholarly literature and
    the increasing inability of researchers to digest, reproduce, reuse its content are
    constant reminders that we urgently need a transformative digitalization of the
    scholarly literature. Building on the Open Research Knowledge Graph (http://orkg.org)
    as a concrete research infrastructure, in this talk we present how using crowdsourcing
    and text mining humans and machines can collaboratively build scholarly knowledge
    bases, i.e. systems that acquire, curate and publish data, information and knowledge
    published in the scholarly literature in structured and semantic form. We discuss some
    key challenges that human and technical infrastructures face as well as the
    possibilities scholarly knowledge bases enable.




Copyright 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

                                                              7

</pre>