dc.identifier.uri |
http://dx.doi.org/10.15488/16296 |
|
dc.identifier.uri |
https://www.repo.uni-hannover.de/handle/123456789/16423 |
|
dc.contributor.author |
Anteghini, Marco
|
|
dc.contributor.author |
D'Souza, Jennifer
|
|
dc.contributor.author |
Dos Santos, Vitor A.P. Martins
|
|
dc.contributor.author |
Auer, Sören
|
|
dc.contributor.editor |
Garijo, Daniel
|
|
dc.contributor.editor |
Lawrynowicz, Agnieszka
|
|
dc.date.accessioned |
2024-02-13T08:26:17Z |
|
dc.date.available |
2024-02-13T08:26:17Z |
|
dc.date.issued |
2020 |
|
dc.identifier.citation |
Anteghini, M.; D'Souza, J.; Dos Santos, V.A.P.M.; Auer, S.: SciBERT-based semantification of bioassays in the open research knowledge graph. In: Garijo, Daniel; Lawrynowicz, Agnieszka (Eds.): EKAW-PD 2020, posters and demonstrations at EKAW 2020 : proceedings of the EKAW 2020 Posters and Demonstrations session, co-located with 22nd International Conference on Knowledge Engineering and Knowledge Management (EKAW 2020). Aachen, Germany : RWTH Aachen, 2020 (CEUR Workshop Proceedings ; 2751), S. 22-30. |
|
dc.description.abstract |
As a novel contribution to the problem of semantifying biological assays, in this paper, we propose a neural-network-based approach to automatically semantify, thereby structure, unstructured bioassay text descriptions. Experimental evaluations, to this end, show promise as the neural-based semantification significantly outperforms a naive frequencybased baseline approach. Specifically, the neural method attains 72% F1 versus 47% F1 from the frequency-based method. The work in this paper aligns with the present cutting-edge trend of the scholarly knowledge digitalization impetus which aim to convert the long-standing document-based format of scholarly content into knowledge graphs (KG). To this end, our selected data domain of bioassays are a prime candidate for structuring into KGs |
eng |
dc.language.iso |
eng |
|
dc.publisher |
Aachen, Germany : RWTH Aachen |
|
dc.relation.ispartof |
EKAW-PD 2020, posters and demonstrations at EKAW 2020 : proceedings of the EKAW 2020 Posters and Demonstrations session, co-located with 22nd International Conference on Knowledge Engineering and Knowledge Management (EKAW 2020) |
|
dc.relation.ispartofseries |
CEUR Workshop Proceedings ; 2751 |
|
dc.relation.uri |
https://ceur-ws.org/Vol-2751/short5.pdf |
|
dc.rights |
CC BY 4.0 Unported |
|
dc.rights.uri |
https://creativecommons.org/licenses/by/4.0 |
|
dc.subject |
Open Science Graphs |
eng |
dc.subject |
Bioassays |
eng |
dc.subject |
Machine Learning |
eng |
dc.subject.classification |
Konferenzschrift |
ger |
dc.subject.ddc |
004 | Informatik
|
|
dc.subject.ddc |
020 | Bibliotheks- und Informationswissenschaft
|
|
dc.title |
SciBERT-based semantification of bioassays in the open research knowledge graph |
eng |
dc.type |
BookPart |
|
dc.type |
Text |
|
dc.relation.essn |
1613-0073 |
|
dc.bibliographicCitation.volume |
2751 |
|
dc.bibliographicCitation.firstPage |
22 |
|
dc.bibliographicCitation.lastPage |
30 |
|
dc.description.version |
publishedVersion |
|
tib.accessRights |
frei zug�nglich |
|