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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

"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."

Název v anglickém jazyce

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

Popis výsledku anglicky

"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."

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

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OECD FORD obor

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

Projekt

—

Návaznosti

—

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

"Journal of the Association for Information Science and Technology"

ISSN

2330-1635

e-ISSN

—

Svazek periodika

74

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

24

Strana od-do

546-569

Kód UT WoS článku

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EID výsledku v databázi Scopus

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