Logo des Repositoriums
 

Reliable Rules for Relation Extraction in a Multimodal Setting

Kurzanzeige
dc.contributor.authorEngelmann, Björn
dc.contributor.authorSchaer, Philipp
dc.contributor.editorKönig-Ries, Birgitta
dc.contributor.editorScherzinger, Stefanie
dc.contributor.editorLehner, Wolfgang
dc.contributor.editorVossen, Gottfried
dc.date.accessioned2023-02-23T14:00:17Z
dc.date.available2023-02-23T14:00:17Z
dc.date.issued2023
dc.description.abstractWe present an approach to extract relations from multimodal documents using a few training data. Furthermore, we derive explanations in the form of extraction rules from the underlying model to ensure the reliability of the extraction. Finally, we will evaluate how reliable (high model fidelity) extracted rules are and which type of classifier is suitable in terms of F1 Score and explainability. Our code and data are available at https://osf.io/dn9hm/?view_only=7e65fd1d4aae44e1802bb5ddd3465e08.en
dc.identifier.doi10.18420/BTW2023-69
dc.identifier.isbn978-3-88579-725-8
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/40378
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBTW 2023
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-331
dc.subjectRelation Extraction
dc.subjectKnowledge Extraction
dc.subjectKnowledge Base Construction
dc.subjectExplainable AI
dc.subjectMultimodal Documents
dc.titleReliable Rules for Relation Extraction in a Multimodal Settingen
dc.typeText/Conference Paper
gi.citation.endPage1021
gi.citation.publisherPlaceBonn
gi.citation.startPage1009
gi.conference.date06.-10. März 2023
gi.conference.locationDresden, Germany

Dateien

Originalbündel
1 - 1 von 1
Vorschaubild nicht verfügbar
Name:
C3-09.pdf
Größe:
555.33 KB
Format:
Adobe Portable Document Format
Herunterladen

Sammlungen

P331 - BTW2023- Datenbanksysteme für Business, Technologie und Web