Extracting the evolutionary backbone of scientific domains: The semantic main path network analysis approach based on citation context analysis.
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...
| Publicado en: | Journal of the Association for Information Science & Technology Vol. 74; no. 5; pp. 546 - 570 |
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| Autores principales: | , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
May2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=162842381&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 162842381 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23301635 H6JN jtl: Journal of the Association for Information Science & Technology issn: 23301635 maglogo: N pubinfo: dt: May2023 vid: 74 iid: 5 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 162842381 162521513 162842381 162842381 10.1002/asi.24748 162842381 ppf: 546 ppct: 24 formats: tig: atl: Extracting the evolutionary backbone of scientific domains: The semantic main path network analysis approach based on citation context analysis. aug: au: Jiang, Xiaorui Liu, Junjun affil: Research Centre for Computational Sciences and Mathematical Modelling, Coventry University, Coventry, UK sug: subj: Information Technology Utilization Citation Analysis Semantics Evaluation Semantic Web Deep Learning Methods Information Science Task Performance and Analysis Human Conceptual Framework Semantic Analysis Motivation Multimethod Studies Linguistics Knowledge Information Management Methods Information Retrieval Methods Path Analysis Funding Source Natural Language Processing Methods Automation Algorithms Electronic Publications Models, Statistical ab: 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‐K 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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