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        <title>A Web-scale system for scientific knowledge exploration</title>
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        <namePart type="given">Zhihong</namePart>
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    <name type="personal">
        <namePart type="given">Kuansan</namePart>
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        <dateIssued>2018-07</dateIssued>
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            <title>Proceedings of ACL 2018, System Demonstrations</title>
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        <name type="personal">
            <namePart type="given">Fei</namePart>
            <namePart type="family">Liu</namePart>
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    <abstract>To enable efficient exploration of Web-scale scientific knowledge, it is necessary to organize scientific publications into a hierarchical concept structure. In this work, we present a large-scale system to (1) identify hundreds of thousands of scientific concepts, (2) tag these identified concepts to hundreds of millions of scientific publications by leveraging both text and graph structure, and (3) build a six-level concept hierarchy with a subsumption-based model. The system builds the most comprehensive cross-domain scientific concept ontology published to date, with more than 200 thousand concepts and over one million relationships.</abstract>
    <identifier type="citekey">shen-etal-2018-web</identifier>
    <identifier type="doi">10.18653/v1/P18-4015</identifier>
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%0 Conference Proceedings
%T A Web-scale system for scientific knowledge exploration
%A Shen, Zhihong
%A Ma, Hao
%A Wang, Kuansan
%Y Liu, Fei
%Y Solorio, Thamar
%S Proceedings of ACL 2018, System Demonstrations
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F shen-etal-2018-web
%X To enable efficient exploration of Web-scale scientific knowledge, it is necessary to organize scientific publications into a hierarchical concept structure. In this work, we present a large-scale system to (1) identify hundreds of thousands of scientific concepts, (2) tag these identified concepts to hundreds of millions of scientific publications by leveraging both text and graph structure, and (3) build a six-level concept hierarchy with a subsumption-based model. The system builds the most comprehensive cross-domain scientific concept ontology published to date, with more than 200 thousand concepts and over one million relationships.
%R 10.18653/v1/P18-4015
%U https://aclanthology.org/P18-4015
%U https://doi.org/10.18653/v1/P18-4015
%P 87-92

Markdown (Informal)

[A Web-scale system for scientific knowledge exploration](https://aclanthology.org/P18-4015) (Shen et al., ACL 2018)

• A Web-scale system for scientific knowledge exploration (Shen et al., ACL 2018)

ACL

• Zhihong Shen, Hao Ma, and Kuansan Wang. 2018. A Web-scale system for scientific knowledge exploration. In Proceedings of ACL 2018, System Demonstrations, pages 87–92, Melbourne, Australia. Association for Computational Linguistics.