Details
| Original language | English |
|---|---|
| Title of host publication | AIxIA 2021 – Advances in Artificial Intelligence - 20th International Conference of the Italian Association for Artificial Intelligence, Revised Selected Papers |
| Editors | Stefania Bandini, Francesca Gasparini, Viviana Mascardi, Matteo Palmonari, Giuseppe Vizzari |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 198-212 |
| Number of pages | 15 |
| ISBN (electronic) | 978-3-031-08421-8 |
| ISBN (print) | 9783031084201 |
| Publication status | Published - 2022 |
| Externally published | Yes |
| Event | 20th International Conference of the Italian Association for Artificial Intelligence, AIxIA 2021 - Virtual, Online Duration: 1 Dec 2021 → 3 Dec 2021 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 13196 LNAI |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. We propose a solution for automatically semantifying biological assays. Our solution contrasts the problem of automated semantification as labeling versus clustering where the two methods are on opposite ends of the method complexity spectrum. Characteristically modeling our problem, we find the clustering solution significantly outperforms a deep neural network state-of-the-art labeling approach. This novel contribution is based on two factors: 1) a learning objective closely modeled after the data outperforms an alternative approach with sophisticated semantic modeling; 2) automatically semantifying biological assays achieves a high performance F1 of nearly 83%, which to our knowledge is the first reported standardized evaluation of the task offering a strong benchmark model.
Keywords
- Automatic semantification, Bioassays, Clustering, Labeling, Open Research Knowledge Graph, Open science graphs, Supervised learning, Unsupervised learning
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
Cite this
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AIxIA 2021 – Advances in Artificial Intelligence - 20th International Conference of the Italian Association for Artificial Intelligence, Revised Selected Papers. ed. / Stefania Bandini; Francesca Gasparini; Viviana Mascardi; Matteo Palmonari; Giuseppe Vizzari. Springer Science and Business Media Deutschland GmbH, 2022. p. 198-212 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13196 LNAI).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Easy Semantification of Bioassays
AU - Anteghini, Marco
AU - D’Souza, Jennifer
AU - dos Santos, Vitor A.P.Martins
AU - Auer, Sören
N1 - Funding Information: Supported by TIB Leibniz Information Centre for Science and Technology, the EU H2020 ERC project ScienceGRaph (GA ID: 819536) and the ITN PERICO (GA ID: 812968).
PY - 2022
Y1 - 2022
N2 - Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. We propose a solution for automatically semantifying biological assays. Our solution contrasts the problem of automated semantification as labeling versus clustering where the two methods are on opposite ends of the method complexity spectrum. Characteristically modeling our problem, we find the clustering solution significantly outperforms a deep neural network state-of-the-art labeling approach. This novel contribution is based on two factors: 1) a learning objective closely modeled after the data outperforms an alternative approach with sophisticated semantic modeling; 2) automatically semantifying biological assays achieves a high performance F1 of nearly 83%, which to our knowledge is the first reported standardized evaluation of the task offering a strong benchmark model.
AB - Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. We propose a solution for automatically semantifying biological assays. Our solution contrasts the problem of automated semantification as labeling versus clustering where the two methods are on opposite ends of the method complexity spectrum. Characteristically modeling our problem, we find the clustering solution significantly outperforms a deep neural network state-of-the-art labeling approach. This novel contribution is based on two factors: 1) a learning objective closely modeled after the data outperforms an alternative approach with sophisticated semantic modeling; 2) automatically semantifying biological assays achieves a high performance F1 of nearly 83%, which to our knowledge is the first reported standardized evaluation of the task offering a strong benchmark model.
KW - Automatic semantification
KW - Bioassays
KW - Clustering
KW - Labeling
KW - Open Research Knowledge Graph
KW - Open science graphs
KW - Supervised learning
KW - Unsupervised learning
UR - http://www.scopus.com/inward/record.url?scp=85135054307&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-08421-8_14
DO - 10.1007/978-3-031-08421-8_14
M3 - Conference contribution
AN - SCOPUS:85135054307
SN - 9783031084201
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 198
EP - 212
BT - AIxIA 2021 – Advances in Artificial Intelligence - 20th International Conference of the Italian Association for Artificial Intelligence, Revised Selected Papers
A2 - Bandini, Stefania
A2 - Gasparini, Francesca
A2 - Mascardi, Viviana
A2 - Palmonari, Matteo
A2 - Vizzari, Giuseppe
PB - Springer Science and Business Media Deutschland GmbH
T2 - 20th International Conference of the Italian Association for Artificial Intelligence, AIxIA 2021
Y2 - 1 December 2021 through 3 December 2021
ER -