Zobrazit v EDS

A Heterogeneous Network Text Attribute Fusion Method Based on Multi-Level Semantic Relation Contrastive Learning

Uloženo v:
Podrobná bibliografie
Název: A Heterogeneous Network Text Attribute Fusion Method Based on Multi-Level Semantic Relation Contrastive Learning
Autoři: Wei Zhang, Zhonglin Ye
Zdroj: International Journal of Data Warehousing and Mining. 21:1-18
Informace o vydavateli: IGI Global, 2025.
Rok vydání: 2025
Popis: Contrastive learning enables models to learn graph structural information through self-supervised learning in the absence of labels. However, real-world networks often contain both graph structural information and incomplete node attribute information. Based on this, this paper proposes a heterogeneous network text attribute fusion method based on multi-layer semantic relation contrastive learning. Firstly, the heterogeneous network components are reconstructed using semantic and thematic attribute acquisition methods at different levels, obtaining semantic representations of text attributes at various levels of abstraction. Then, the contrastive learning component of the heterogeneous network is employed to maximize the correlation between different views of the heterogeneous network, allowing the two heterogeneous networks to align in this space. This alignment helps to uncover the latent connections between text attribute features across different views, thereby achieving the fusion of information between views.
Druh dokumentu: Article
Jazyk: Ndonga
ISSN: 1548-3932
1548-3924
DOI: 10.4018/ijdwm.378680
Rights: CC BY
Přístupové číslo: edsair.doi...........b428ea3aa9d68faf907637856548aed7
Databáze: OpenAIRE
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://resolver.ebscohost.com/openurl?sid=EBSCO:edsair&genre=article&issn=15483932&ISBN=&volume=21&issue=&date=20250610&spage=1&pages=1-18&title=International Journal of Data Warehousing and Mining&atitle=A%20Heterogeneous%20Network%20Text%20Attribute%20Fusion%20Method%20Based%20on%20Multi-Level%20Semantic%20Relation%20Contrastive%20Learning&aulast=Wei%20Zhang&id=DOI:10.4018/ijdwm.378680
    Name: Full Text Finder
    Category: fullText
    Text: Full Text Finder
    Icon: https://imageserver.ebscohost.com/branding/images/FTF.gif
    MouseOverText: Full Text Finder
  – Url: https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=EBSCO&SrcAuth=EBSCO&DestApp=WOS&ServiceName=TransferToWoS&DestLinkType=GeneralSearchSummary&Func=Links&author=Zhang%20W
    Name: ISI
    Category: fullText
    Text: Nájsť tento článok vo Web of Science
    Icon: https://imagesrvr.epnet.com/ls/20docs.gif
    MouseOverText: Nájsť tento článok vo Web of Science
Header DbId: edsair
DbLabel: OpenAIRE
An: edsair.doi...........b428ea3aa9d68faf907637856548aed7
RelevancyScore: 1033
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 1032.50927734375
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: A Heterogeneous Network Text Attribute Fusion Method Based on Multi-Level Semantic Relation Contrastive Learning
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Wei+Zhang%22">Wei Zhang</searchLink><br /><searchLink fieldCode="AR" term="%22Zhonglin+Ye%22">Zhonglin Ye</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <i>International Journal of Data Warehousing and Mining</i>. 21:1-18
– Name: Publisher
  Label: Publisher Information
  Group: PubInfo
  Data: IGI Global, 2025.
– Name: DatePubCY
  Label: Publication Year
  Group: Date
  Data: 2025
– Name: Abstract
  Label: Description
  Group: Ab
  Data: Contrastive learning enables models to learn graph structural information through self-supervised learning in the absence of labels. However, real-world networks often contain both graph structural information and incomplete node attribute information. Based on this, this paper proposes a heterogeneous network text attribute fusion method based on multi-layer semantic relation contrastive learning. Firstly, the heterogeneous network components are reconstructed using semantic and thematic attribute acquisition methods at different levels, obtaining semantic representations of text attributes at various levels of abstraction. Then, the contrastive learning component of the heterogeneous network is employed to maximize the correlation between different views of the heterogeneous network, allowing the two heterogeneous networks to align in this space. This alignment helps to uncover the latent connections between text attribute features across different views, thereby achieving the fusion of information between views.
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Article
– Name: Language
  Label: Language
  Group: Lang
  Data: Ndonga
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1548-3932<br />1548-3924
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.4018/ijdwm.378680
– Name: Copyright
  Label: Rights
  Group: Cpyrght
  Data: CC BY
– Name: AN
  Label: Accession Number
  Group: ID
  Data: edsair.doi...........b428ea3aa9d68faf907637856548aed7
PLink https://erproxy.cvtisr.sk/sfx/access?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsair&AN=edsair.doi...........b428ea3aa9d68faf907637856548aed7
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.4018/ijdwm.378680
    Languages:
      – Text: Ndonga
    PhysicalDescription:
      Pagination:
        PageCount: 18
        StartPage: 1
    Titles:
      – TitleFull: A Heterogeneous Network Text Attribute Fusion Method Based on Multi-Level Semantic Relation Contrastive Learning
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Wei Zhang
      – PersonEntity:
          Name:
            NameFull: Zhonglin Ye
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 10
              M: 06
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 15483932
            – Type: issn-print
              Value: 15483924
            – Type: issn-locals
              Value: edsair
            – Type: issn-locals
              Value: edsairFT
          Numbering:
            – Type: volume
              Value: 21
          Titles:
            – TitleFull: International Journal of Data Warehousing and Mining
              Type: main
ResultId 1