Easy Semantification of Bioassays

dc.bibliographicCitation.bookTitleAIxIA 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.date2022
dc.bibliographicCitation.firstPage198
dc.bibliographicCitation.lastPage212
dc.bibliographicCitation.seriesTitleLecture Notes in Computer Science ; 13196eng
dc.bibliographicCitation.volume13196
dc.contributor.authorAnteghini, Marco
dc.contributor.authorD’Souza, Jennifer
dc.contributor.authordos Santos, Vitor A. P. Martins
dc.contributor.authorAuer, Sören
dc.date.accessioned2024-05-10T05:24:24Z
dc.date.available2024-05-10T05:24:24Z
dc.date.issued2022
dc.description.abstractBiological 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.versionacceptedVersioneng
dc.identifier.urihttps://oa.tib.eu/renate/handle/123456789/14591
dc.identifier.urihttps://doi.org/10.34657/13622
dc.language.isoeng
dc.publisherHeidelberg : Springer
dc.relation.doihttps://doi.org/10.1007/978-3-031-08421-8_14
dc.relation.essn1611-3349
dc.relation.issn0302-9743
dc.rights.licenseThis 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.licenseDieses 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.ddc004
dc.subject.gndKonferenzschriftger
dc.subject.otherAutomatic semantificationeng
dc.subject.otherBioassayseng
dc.subject.otherClusteringeng
dc.subject.otherLabelingeng
dc.subject.otherOpen Research Knowledge Grapheng
dc.subject.otherOpen science graphseng
dc.subject.otherSupervised learningeng
dc.subject.otherUnsupervised learningeng
dc.titleEasy Semantification of Bioassayseng
dc.typeBookParteng
dc.typeTexteng
dcterms.event20th International Conference of the Italian Association for Artificial Intelligence, AIxIA 2021, 1 December 2021-3 December 2021, Virtual, Online
tib.accessRightsopenAccess
wgl.contributorTIB
wgl.subjectInformatikger
wgl.typeBuchkapitel / Sammelwerksbeitragger
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