Details
| Original language | English |
|---|---|
| Title of host publication | Posters and Demonstrations at EKAW 2020 |
| Subtitle of host publication | Proceedings of the EKAW 2020 Posters and Demonstrations Session co-located with 22nd International Conference on Knowledge Engineering and Knowledge Management (EKAW 2020) |
| Pages | 22-30 |
| Number of pages | 9 |
| Publication status | Published - 2020 |
| Externally published | Yes |
| Event | 22nd International Conference on Knowledge Engineering and Knowledge Management - Posters and Demonstrations Session, EKAW-PD 2020 - Virtual, Bozen-Bolzano, Italy Duration: 16 Sept 2020 → 18 Sept 2020 |
Publication series
| Name | CEUR Workshop Proceedings |
|---|---|
| Publisher | CEUR Workshop Proceedings |
| Volume | 2751 |
| ISSN (Print) | 1613-0073 |
Abstract
As a novel contribution to the problem of semantifying bio- logical 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.
Keywords
- Bioassays, Machine Learning, Open Science Graphs
ASJC Scopus subject areas
- Computer Science(all)
- General Computer Science
Cite this
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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) . 2020. p. 22-30 (CEUR Workshop Proceedings; Vol. 2751).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - SciBERT-based Semantification of Bioassays in the Open Research Knowledge Graph
AU - Anteghini, Marco
AU - D'Souza, Jennifer
AU - Dos Santos, Vitor A.P.Martins
AU - Auer, Sören
PY - 2020
Y1 - 2020
N2 - As a novel contribution to the problem of semantifying bio- logical 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.
AB - As a novel contribution to the problem of semantifying bio- logical 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.
KW - Bioassays
KW - Machine Learning
KW - Open Science Graphs
UR - http://www.scopus.com/inward/record.url?scp=85097291239&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2009.08801
DO - 10.48550/arXiv.2009.08801
M3 - Conference contribution
AN - SCOPUS:85097291239
T3 - CEUR Workshop Proceedings
SP - 22
EP - 30
BT - Posters and Demonstrations at EKAW 2020
T2 - 22nd International Conference on Knowledge Engineering and Knowledge Management - Posters and Demonstrations Session, EKAW-PD 2020
Y2 - 16 September 2020 through 18 September 2020
ER -