Extracting the evolutionary backbone of scientific domains: The semantic main path network analysis approach based on citation context analysis

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AM8YN29IT" target="_blank" >RIV/00216208:11320/23:M8YN29IT - isvavai.cz</a>

Result on the web

<a href="https://asistdl.onlinelibrary.wiley.com/doi/10.1002/asi.24748" target="_blank" >https://asistdl.onlinelibrary.wiley.com/doi/10.1002/asi.24748</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/asi.24748" target="_blank" >10.1002/asi.24748</a>

Result language

angličtina

Original language name

Extracting the evolutionary backbone of scientific domains: The semantic main path network analysis approach based on citation context analysis

Original language description

"Abstractn n Main path analysis is a popular method for extracting the scientific backbone from the citation network of a research domain. Existing approaches ignored the semantic relationships between the citing and cited publications, resulting in several adverse issues, in terms of coherence of main paths and coverage of significant studies. This paper advocated the semantic main path network analysis approach to alleviate these issues based on citation function analysis. A wide variety of SciBERT‐based deep learning models were designed for identifying citation functions. Semantic citation networks were built by either including important citations, for example, extension, motivation, usage and similarity, or excluding incidental citations like background and future work. Semantic main path network was built by merging the top‐n Kn main paths extracted from various time slices of semantic citation network. In addition, a three‐way framework was proposed for the quantitative evaluation of main path analysis results. Both qualitative and quantitative analysis on three research areas of computational linguistics demonstrated that, compared to semantics‐agnostic counterparts, different types of semantic main path networks provide complementary views of scientific knowledge flows. Combining them together, we obtained a more precise and comprehensive picture of domain evolution and uncover more coherent development pathways between scientific ideas."

Czech name

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Czech description

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Type

J_ost - Miscellaneous article in a specialist periodical

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

"Journal of the Association for Information Science and Technology"

ISSN

2330-1635

e-ISSN

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Volume of the periodical

74

Issue of the periodical within the volume

5

Country of publishing house

US - UNITED STATES

Number of pages

24

Pages from-to

546-569

UT code for WoS article

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EID of the result in the Scopus database

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