dc.identifier.uri |
http://dx.doi.org/10.15488/16799 |
|
dc.identifier.uri |
https://www.repo.uni-hannover.de/handle/123456789/16926 |
|
dc.contributor.author |
Jaradeh, Mohamad Yaser
|
|
dc.contributor.author |
Singh, Kuldeep
|
|
dc.contributor.author |
Stocker, Markus
|
|
dc.contributor.author |
Auer, Sören
|
|
dc.contributor.editor |
Gentile, A.L.
|
|
dc.contributor.editor |
Gonçalves, R.
|
|
dc.date.accessioned |
2024-03-26T09:31:15Z |
|
dc.date.available |
2024-03-26T09:31:15Z |
|
dc.date.issued |
2021 |
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dc.identifier.citation |
Jaradeh, M.Y.; Singh, K.; Stocker, M.; Auer, S.: Better Call the Plumber: Triple Classification for Scholarly Knowledge Graph Completion. In: Gentile, A.L.; Gonçalves,R. (Eds.): K-CAP '21: Proceedings of the 11th Knowledge Capture Conference. New York, NY : Association for Computing Machinery, 2021, S. 225-232. DOI: https://doi.org/10.1145/3460210.3493582 |
|
dc.description.abstract |
structured information representing knowledge encoded in scientific publications. With the sheer volume of published scientific literature comprising a plethora of inhomogeneous entities and relations to describe scientific concepts, these KGs are inherently incomplete. We present exBERT, a method for leveraging pre-trained transformer language models to perform scholarly knowledge graph completion. We model triples of a knowledge graph as text and perform triple classification (i.e., belongs to KG or not). The evaluation shows that exBERT outperforms other baselines on three scholarly KG completion datasets in the tasks of triple classification, link prediction, and relation prediction. Furthermore, we present two scholarly datasets as resources for the research community, collected from public KGs and online resources. |
eng |
dc.language.iso |
eng |
|
dc.publisher |
New York, NY : Association for Computing Machinery |
|
dc.relation.ispartof |
K-CAP '21: Proceedings of the 11th Knowledge Capture Conference |
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dc.rights |
This document may be downloaded, read, stored and printed for your own use within the limits of § 53 UrhG but it may not be distributed on other websites via the internet or passed on to external parties. |
eng |
dc.rights |
Dieses Dokument darf im Rahmen von § 53 UrhG zum eigenen Gebrauch kostenfrei heruntergeladen, gelesen, gespeichert und ausgedruckt, aber nicht auf anderen Webseiten im Internet bereitgestellt oder an Außenstehende weitergegeben werden. |
ger |
dc.subject |
link prediction |
eng |
dc.subject |
relation prediction |
eng |
dc.subject |
scholarly knowledge graphs |
eng |
dc.subject |
triple classification |
eng |
dc.subject.classification |
Konferenzschrift |
ger |
dc.subject.ddc |
004 | Informatik
|
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dc.title |
Triple Classification for Scholarly Knowledge Graph Completion |
eng |
dc.type |
BookPart |
|
dc.type |
Text |
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dc.relation.isbn |
978-1-4503-8457-5 |
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dc.relation.doi |
https://doi.org/10.1145/3460210.3493582 |
|
dc.bibliographicCitation.firstPage |
225 |
|
dc.bibliographicCitation.lastPage |
232 |
|
dc.description.version |
acceptedVersion |
eng |
tib.accessRights |
frei zug�nglich |
|