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Relation semantic fusion in subgraph for inductive link prediction in knowledge graphs

Relation semantic fusion in subgraph for inductive link prediction in knowledge graphs

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_b6fd203a40bc4b669fca95f7de99f406

Relation semantic fusion in subgraph for inductive link prediction in knowledge graphs

About this item

Full title

Relation semantic fusion in subgraph for inductive link prediction in knowledge graphs

Publisher

United States: PeerJ. Ltd

Journal title

PeerJ. Computer science, 2024-10, Vol.10, p.e2324, Article e2324

Language

English

Formats

Publication information

Publisher

United States: PeerJ. Ltd

More information

Scope and Contents

Contents

Inductive link prediction (ILP) in knowledge graphs (KGs) aims to predict missing links between entities that were not seen during the training phase. Recent some subgraph-based methods have shown some advancements, but they all overlook the relational semantics between entities during subgraph extraction. To overcome this limitation, we introduce...

Alternative Titles

Full title

Relation semantic fusion in subgraph for inductive link prediction in knowledge graphs

Authors, Artists and Contributors

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_doaj_primary_oai_doaj_org_article_b6fd203a40bc4b669fca95f7de99f406

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_b6fd203a40bc4b669fca95f7de99f406

Other Identifiers

ISSN

2376-5992

E-ISSN

2376-5992

DOI

10.7717/peerj-cs.2324

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