Easy Semantification of Bioassays
dc.bibliographicCitation.bookTitle | AIxIA 2021 – Advances in Artificial Intelligence : 20th International Conference of the Italian Association for Artificial Intelligence, Virtual Event, December 1–3, 2021, Revised Selected Papers | |
dc.bibliographicCitation.date | 2022 | |
dc.bibliographicCitation.firstPage | 198 | |
dc.bibliographicCitation.lastPage | 212 | |
dc.bibliographicCitation.seriesTitle | Lecture Notes in Computer Science ; 13196 | eng |
dc.bibliographicCitation.volume | 13196 | |
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.date.accessioned | 2024-05-10T05:24:24Z | |
dc.date.available | 2024-05-10T05:24:24Z | |
dc.date.issued | 2022 | |
dc.description.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. | eng |
dc.description.version | acceptedVersion | eng |
dc.identifier.uri | https://oa.tib.eu/renate/handle/123456789/14591 | |
dc.identifier.uri | https://doi.org/10.34657/13622 | |
dc.language.iso | eng | |
dc.publisher | Heidelberg : Springer | |
dc.relation.doi | https://doi.org/10.1007/978-3-031-08421-8_14 | |
dc.relation.essn | 1611-3349 | |
dc.relation.issn | 0302-9743 | |
dc.rights.license | 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.license | 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.ddc | 004 | |
dc.subject.gnd | Konferenzschrift | ger |
dc.subject.other | Automatic semantification | eng |
dc.subject.other | Bioassays | eng |
dc.subject.other | Clustering | eng |
dc.subject.other | Labeling | eng |
dc.subject.other | Open Research Knowledge Graph | eng |
dc.subject.other | Open science graphs | eng |
dc.subject.other | Supervised learning | eng |
dc.subject.other | Unsupervised learning | eng |
dc.title | Easy Semantification of Bioassays | eng |
dc.type | BookPart | eng |
dc.type | Text | eng |
dcterms.event | 20th International Conference of the Italian Association for Artificial Intelligence, AIxIA 2021, 1 December 2021-3 December 2021, Virtual, Online | |
tib.accessRights | openAccess | |
wgl.contributor | TIB | |
wgl.subject | Informatik | ger |
wgl.type | Buchkapitel / Sammelwerksbeitrag | ger |
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